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Jason De Oliveira works as a CTO for MEGA International, a software company in Paris (France), providing modeling tools for business transformation, enterprise architecture, and enterprise governance, risk, and compliance management. He is an experienced manager and senior solutions architect, with high skills in software architecture and enterprise architecture. He has been awarded him MVP C#/.NET by Microsoft for over 6 years for his numerous contributions to the Microsoft community. In this interview, Jason talks about the newly introduced features of .NET Core 2.0 and how they empower effective cross-platform application development. He also gives us a sneak-peek of his recently released book Learning ASP.NET Core 2.0 Packt: Let's start with a very basic question. What is .NET Core? How is it different from the .NET Framework? Jason De Oliveira: In the last 20 years, Microsoft has focused mainly on Windows and built many technologies around it. You can see that easily when looking at the ASP.NET and the .NET Framework in general. They provide a very good integration and extend the operating system for building great desktop and web applications, but only on the Windows platform. In the last 5 years, we have seen some major changes in the overall strategy and vision of Microsoft due to major changes in the IT market. The cloud-first as well as the mobile-first strategy coupled with the support for other operating systems has lead Microsoft to completely rethink most of the existing frameworks - including the .NET Framework. That is one of the major reasons why the .NET Core framework came into existence. It incorporates a new way of building applications, fully embracing multi-platform development and the latest standards and technologies - and what a great framework it has turned out to be! ASP .Net Core 2.0 was recently announced in August 2017. What are the new features and updates introduced in this release to make the development of web apps easier? The latest version of ASP.NET Core is 2.0, which, provides much better performance than the other versions before it. It has been open-sourced, so developers can understand how it works internally and adapt it easily to specific needs if necessary. Furthermore, the integration between .NET Core and Visual Studio has been improved. Some other important features of this new release are: The Meta-Package which includes everything necessary to develop great web applications has been added to the framework An improved NuGet compatibility has been added. This leads to much better developer productivity and efficiency. Web development can be fun and using .NET Core in conjunction with the latest version of Visual Studio proves it! What benefits does Entity Framework Core 2 offer while building a robust MVC web app? When you are building web applications, you need to store your data somewhere. Today, most of the time the data is stored in relational databases (Microsoft SQL Server, Oracle and so on). While there are multiple ways of connecting to a database using ASP.NET Core, we advise using Entity Framework Core 2, because of its simplicity and because it contains all necessary features already built-in. Another big advantage is that it abstracts the database structure from the object-oriented structure within your code (also commonly called ORM). It furthermore supports nearly all databases you can find on the market either directly or via additional providers. When it comes to .NET Core, do you think there is any scope for improvement? What can Microsoft do in the future to improve the suite? To be honest ASP.NET Core 2.0 has achieved a very high level of feature coverage. Nearly everything you can think of is included by default, which is quite remarkable. Nevertheless, Microsoft has already shipped an updated version called ASP.NET Core 2.1 and we can expect that it will further support and evolve the framework. However, an area of improvement could be Artificial Intelligence (AI). As you might know, Microsoft is currently investing very strongly in this area and we think that we might see some features getting included with the next versions of ASP.NET Core. Also in terms of testability of code and especially live unit testing, we hope to see some improvements in the future. Unit tests are important for building high quality application with less bugs, so having a thorough integration of ASP.NET Core with the Visual Studio Testing tools would be a big advantage for any developer. Tell us something about your book. What makes it unique? With this book, you will learn and have fun at the same time. The book is very easy to read, while containing basic and advanced concepts of ASP.NET Core 2.0 web development. It is not only about the development, though. You will also see how to apply agile methodologies and use tools such as Visual Studio and Visual Studio Code. The examples in the book are real world examples, which have been added to build a real application at the end. Not just stripped down sample examples, but instead examples that you can adapt to your own application needs quickly. From the start to the end of the book we have applied a Minimum Viable Product (MVP) approach, meaning that at the end of each chapter you will have evolved the overall sample application a little bit more. This is motivating and interesting and will keep you hooked. You will see how to work in different environments such as on-premises and in the cloud. We explain how to deploy, manage and supervise your ASP.NET Core 2.0 web application in Microsoft Azure, Amazon and Docker, for example. For someone new to web development, what learning path in terms of technologies would you recommend? What is the choice of tools he/she should make to build the best possible web applications? Where does .NET Core 2.0 fit into the picture? There are different types of web developers, who could take potentially different paths. Some will start with the graphical user interface and be more interested in the graphical representation of a web application. They should start with HTML5 and CSS3 and maybe use JavaScript to handle the communication with the server. Then, there are API developers, who are not very interested in graphical representation but more in building efficient API and web services, which allow reducing bandwidth and providing good performance. In our book, we show the readers how to build views and controllers, while using the latest frontend technologies to provide a modern look and feel. We then explain how to use the built-in features of ASP.NET Core 2.0 for building Web APIs and how to connect them via Entity Framework 2 with the database or provide a whole host of other services (such as sending emails, for example).

School of Code is a company on a mission to help more people benefit from technology. And Packt are delighted to support the initiative. Empowering people to master code and feel confident developing software is central to Packt's mission, so it is fantastic to partner with an organization doing just that in the West Midlands.  We were lucky enough to speak to Nazia Choudrey who has been involved with the School of Code bootcamp. She told us about her experience with the initiative, and her perspective on what tech is like today for women and people of color.  Packt: Hi Nazia! Tell us a little about yourself. Nazia Choudrey: I am a person who is trying to re-invent myself. Just over a year ago I walked away from my so-called life to start afresh. I am not a person who looks back or has regrets, because everything that has happened is what has made me the person I am today – a person who has no fears and who is willing to try new things.  What were you doing before you enrolled for School of Code and what made you want to sign up? I was a probation court officer for 8 years, where I dealt with the day to day running of things especially focused on breaches. Even though I was on a computer every day I knew very little about how they worked, and more importantly, why they crashed on me. I guess that pursuit is what lead me to school of Code. What are you looking to gain from studying at School of Code in the long run? I’m looking to make computers my friend and, to be honest, to make a career in coding. With these new-found skills, I’ll hopefully be able to help others, develop products and software to help others gain a good standard of living which we are all entitled to. Do you think there is a diversity issue in the tech sector? Has it affected you in any way? I do think there is a diversity issue, as when most think about tech, they think of a white male person, an introvert, not a female from a minority group. Even I had that impression and therefore thought that this sector was not for me, but then I love to challenge sterotypes. I thought why not, let’s give it a try as I have nothing to lose. Why do you think making technology accessible to all is important? This is very important as, technology is taking over the world. Within the next 10-15 years all the jobs will be in this sector. Therefore, we all need to be ready to accept tech beyond our everyday lives. What do you think the future looks like for people working in the tech industry? Will larger companies strive to diversify their workforce, and, why should they? I think there is a bright future for people in the tech industry, I also think that all jobs will have some sort of tech that is needed. So therefore, this will need to be a diverse place where people from all sectors of life can come together. If large companies don't strive to be diverse they will be losing out on a large resource of social skills, of soft skills that diverse people can bring to the work place, putting them in positions to be global companies. School of Code encourages the honing of soft skills through networking, team work and project management. Do you think these skills are vital for the future of the Tech industry and attracting a new generation, shaking off the stereotype that all coders are solitary beings? Why? SoC helps and encourages people to work as a team, without the rat race to the finish. Find out more about School of Code  Download some of the books the Bootcampers found useful during the course: Thinking in HTML Thinking in CSS Thinking in JS series  MEAN Web Development React and React Native Responsive Web Design

School of Code is a company on a mission to help more people benefit from technology. It has created an online multiplayer platform that aims to make coding fun, simple and accessible to all. This platform has been used by over 120,000 people since its launch in December 2016, and School of Code recently won the ‘Transforming Lives’ award at the 2017 Education Awards. The company was founded by Chris Meah while he was completing his PhD in Computer Science at the University of Birmingham.  As headline sponsors, Packt founder and CEO Dave Maclean shares his thoughts on the programme. “The number and diversity of the applicants proves how many people in Birmingham are looking to learn key skills like HTML, CSS, Javascript and Node.JS. Packt is excited to sponsor School of Code’s Bootcamp participants to increase the population of skilled developers in the West Midlands, which will have an impact on the growth of innovative start-ups in this region.” We spoke to Sharon Kaur, who's been involved with a School of Code bootcamp about her experience and for her perspective on tech in 2018. Packt: Hi Sharon! Tell us a little about yourself. Sharon Kaur: My name is Sharon. I am a choreographer and dancer for international music groups. I am also an engineering and technology advocate and STEM Ambassador for the UK and India – my main aim is getting more young girls and ethnic minorities interested in and pursuing a career in science, technology and engineering. What were you doing before you enrolled for School of Code and what made you want to sign up? I previously studied my BEng honours and MSc degrees at University of Surrey, in general and medical engineering. I worked in the STEM education industry for a few years and then gained my teaching qualification in secondary school/sixth form Science in Birmingham. I recently started learning more about the technology industry after completing an online distance-learning course in cyber security. I was on Facebook one day in June and I saw an advert for the first ever School of Code Bootcamp, and I just decided to dive in and go for it! Do you think there is a diversity issue in the tech sector? Has it affected you in any way? I definitely think there is a major problem in the technology industry, in terms of diversity. There are far too many leadership and management positions taken up by upper/middle class, white men. There needs to be more outreach work done to attract more women and ethnic minority people into this sector, as well as continuing to work with them afterwards, to prevent them from leaving tech in the middle of their careers! This has not affected me in any direct way, but as a female from an engineering background, which is also a very male-dominated sector, I have experienced some gender discrimination and credit for work I produced being given to someone else. Why do you think making technology accessible to all is important? Technology opens up so many doors to some really exciting and life-fulfilling work. It really is the future of this planet, and in order to keep improving the progress of the global economy and human society, we need more and more advanced technology and methods, daily. This means that there is a dire need for a large number of highly competent employees working continuously in the tech sector. What do you think the future looks like for people working in the tech industry? Will larger companies strive to diversify their workforce, and, why should they? In my opinion, the future looks extremely exciting and progressive! Technology will only become more and more futuristic, and we could be looking at getting more into the sci-fi age, come the next few centuries, give or take. So, the people who will work in the tech sector will be highly sought after – lucky them! I would hope though, that large corporations will change their employee recruitment policies, in terms of a more diverse intake, if they truly want to reach the top of their games, with maximum efficiency and employee wellbeing. School of Code encourages the honing of soft skills through networking, team work and project management. Do you think these skills are vital for the future of the tech industry and attracting a new generation, shaking off the stereotype that all coders are solitary beings? Why? Yes, definitely – soft skills are just as important, if not slightly more, than the technical aptitude of an employee in the tech industry! With collaboration and a business acumen, we can bring the world of technology together and use it to make a better life for every human being on this planet. The technology industry needs to show its solidarity, not its divisiveness, in attracting the next generation of young techies, if it wants to maintain its global outreach. What advice would you give to someone who wanted to get into the tech sector but may be put off by the common preconception that it is made up of male white privilege? I would say go for it, dive in at the deep end and come out the other side the better person in the room! Have the courage to stand up for your beliefs and dreams, and don't ever let anyone tell you or make you feel like you don't deserve to be standing there with everyone else in the room – pick your battles wisely, become more industry – and people-savvy, choose your opportune moment to shine, and you'll see all the other techies begging you to work with them, not even for them! Find out more about School of Code.  Download some of the books the Bootcampers found useful during the course: Thinking in HTML Thinking in CSS Thinking in JS series  MEAN Web Development React and React Native Responsive Web Design

Claire Chung is a PhD student at the Chinese University of Hong Kong researching bioinformatics. She is also one of the authors of Matplotlib 2.0 by Example. We spoke to Claire about her experience in tech, and her book. She offered a unique perspective on gender issues in the industry as well as wider questions of accessibility and the future of research and how technology will continue to change the way we make scientific discoveries. Packt: What’s the most interesting thing/key take away from your book? Claire Chung: Good storytelling is crucial even for the best data crunching results. Data visualization determines whether you can actually get the message across in presentation. This is true not just for the Tech field, but in any workplace setting - to persuade your clients, your boss, or simply to communicate objective quantitative concepts to fellow scientists or even the general public. Many people around feel scared when it comes to the word ‘code’, or think that numbers alone can provide a complete picture, so sometimes they turn to unsuitable plots that are easily produced with common software, leaving them not customized. We want to tell everyone the right plot and refinement of your data graphics do matter, and it doesn’t take superb programming skills or expensive software to create brilliant data visualization. Packt: Tell us about your experience in the Tech sector, how did you get where you are today and how did you come to write your book with Packt? Did you experience any setbacks along the way? CC: How I went from a bioinformatics lab to writing a Packt book with a lab alumnae today is a bit like joining dots together. Computers have interested me since I was small, because the internet opened up a brave new world of unseen knowledge that was free for me to explore. Moreover, I find huge fun and satisfaction in using logic and creativity in problem solving. I remember persuading my mum at a younger age to let me replace the graphic card based on my own diagnosis, instead of paying double for just a check excluding repair fee, and solved the issue swiftly. Whenever there are technical problems, family, teachers and friends would usually come to me. I enjoy helping them fix the issues, with explanations so they understand how to deal with similar situations. At that time, I did not particularly aspire to be in the Tech field. Sometimes I think that if I wasn’t a scientist, I might have been a dancer or tour guide or anything else. However,  with my curiosity focused on exploring the fascinating deep mysteries of life, I chose to read Cell and Molecular Biology in college. During my undergraduate study, I saw that the Campus Network Support Team in the IT department of my college was recruiting student helpers. I took the opportunity to test self-learnt skills, and gratefully was accepted after the apt-test and interview. While most members naturally come from the Computer Science and Engineering faculty, and were mostly male, I observed a very good work culture there. Everyone was friendly with and willing to learn from each other, regardless of fields of study, education background, gender or anything else. We became good partners at work and good friends in life. Later, I had the privilege to be appointed as leader of the team, responsible for coordinating team effort, recruitment and training of new members, as well as communicating with senior staff and operators in providing support service. This friendly atmosphere and valuable experience play a part in giving me confidence to work in the Tech field and faith that a bias-free friendly environment is totally achievable. In pursuing my studies in life science, I found out that biological data plays a huge role in driving data science. Traditional approaches of genetic research, for instance, study gene actions one by one. Not only is this largely time and labour intensive, we can easily miss the big picture of the network of interactions between genes and different components in the multiple layers of regulations.  Like other Tech fields, technological advancement makes it much easier to generate data, computational power has also increased exponentially in recent decade to power, but data analysis is still at bottleneck. The amount of skilful analysts cannot keep up with the immeasurable data generated each day. While we do not replace traditional studies being of different scope and the basis of research in any scale, there is much more effort needed to innovate for better delineating biology, the workings of life. Packt: How has the industry changed since you started working? Do you think it is getting easier for people from different backgrounds to get into the Tech world? CC: I am working in bioinformatics and have met people in all walks of life getting into Tech. In my point of view, it is certainly evolving into a more open field. Decades ago, programming and data analysis skills used to be vocational expertise. Computer engineering students graduate into programmers in companies. They mostly work in technology-dedicated companies, or are appointed or sent to attach to existing departments on demand. This model still persists, but I have also observed a large paradigm shift nowadays. Companies are hiring more people from diverse background. Startups and new teams in large corporations with high degree of freedom keep emerging. I have friends with art training developing UX award entry apps. I met people with physics, chemistry and civil engineering education and various level of coding background in the same team when speaking in a Tech conference. Today, like how typists have almost disappeared entirely, office productivity software usage is expected as much as basic English writing and arithmetic, basic programming skills are gradually become some sort of literacy. In my case, I am working in a lab among the first established bioinformatics lab in Hong Kong. Not long before I enter the lab, there are so few bioinformaticians, that coming to our lab is one of the very few local options to go when you want to analyse any sequencing data at all. Today we still present an obvious edge in being the most familiar with these techniques, to critically assess and provide solutions to different studies, and hence the most ready to further explore beyond basic analysis, so my supervisor continues to help us select collaboration to get the most interesting questions answered. On the other hand, with increasing availability of courses on bioinformatics and data science, user-friendly packages for data analysis, laboratories are able to hire students who are willing to learn from trial-and-error, or purchase commercial services for basic or routine analyses. While knowledge and techniques set the concrete foundation for anything to build on, I feel that there are no more technical skills that are irreplaceable life-long. Innovation, human-related and overall management skills make the difference. I was really a sidekick at the time I started; there were only two girls including myself. But guess what? We have more or less even males and females joining as regular students now. Packt: What are the biggest misconceptions about working in the Tech sector today? CC: I think the biggest misconceptions are “I am not …, I will never do/understand it”. But more specifically, there seems to be a belief that you must hold a degree in Computer Science or Engineering in order to start your career in the Tech sector today. However, this concept probably isn't as strong in the field as it is outside. Many people outside the field got extremely curious, how I am working in front of computers as a student under a biology programme. While formal major study is undeniably the most direct way to obtain solid foundation of knowledge and to enter the field, it is not a definite ticket to success in the industry. The reverse is true as well. The “Tech field” today is not a shrouded dome. It is a hub where people pass through in getting to their destination. You can start off with a strong computation background, and that can help you land on many different areas. Besides the low-cost learning materials around mentioned, more and more attachment programmes are welcoming aspiring apprentices to bring in new sparks. Think in the other way, starting with less releases you from high opportunity cost. You can also invest & publish your work on GitHub or app stores. Results speak it all. I believe fresher minds on holiday embarking today can probably come up with more brilliant ideas, than I can do with limited time outside my PhD study. We should not be intimidated by things like “I am not white”, “I am not a guy”, “I have not tried this before”, etc. Instead, think of “how you will work it out”. We also should not be bounded by stereotypes against ourselves or anyone else. Personal non-work related qualities and hobbies are irrelevant to ability. (I always wear dress or skirts around in campus or in tech conferences simply because I like it.) Of course, we never need the whole world to work in the same sector, which is not healthy for individuals and society at all. Just don’t set a limit for ourselves. If you want to dive in, be prepared. Then “knock, and the door will be opened.” Packt: What do you think the future looks like for people working in the Tech industry? Will larger companies strive to make it more accessible to everyone? CC: I think the future of the Tech industry will get ever more open and competitive. Will larger companies strive to make it more accessible to everyone? Sure. I think they are doing so even right now. Look at the moves taken at Google cloud platform, Mircosoft STEM resources and NVIDIA’s deep learning institute, just to name a few, including online courses they sponsored. The industry is always in thirst of talent. More open resources bring in more brilliant minds to keep advancement. As discussed, there will no longer be a clear gap set by hours spent or grades obtained in classroom, but willingness to learn and apply actively. Even for people with the strongest formal engineering training, this is the attitude that will push them forward towards far greater success. I am looking forward to seeing more vibrant energy in the field in coming future. Thanks for chatting with us Claire! Check out Matplotlib 2.0 by Example here. Or explore more data analysis resources here.

If you're not sure what Spring Boot is exactly, or why it's becoming such an important part of the Java development ecosystem you're in the right place. We'll explain: Spring Boot is a micro framework built by the team at Pivotal that has been designed to simplify the bootstrapping and development of new Spring applications. To put it simply, it gets you up and running as quickly as possible. Greg Turnquist has has significant experience with the Spring team at Pivotal for some time - which means he is in the perfect position to give an insight on the software. We spoke to Greg recently to shed some light on Spring Boot, as well as his latest book Learning Spring Boot 2.0 - Second Edition. Greg on tweets as @gregturn on Twitter. Why you should use Spring Boot Packt: Spring Boot is a popular tool for building production-grade enterprise applications in Spring. What do you think are the 3 notable features of Spring Boot that stands apart from the other tools available out there? Greg Turnquist: The three characteristics I have found interesting are: Simplicity and ease to build new apps Boot's ability to back off when you define custom components, and Boot's ability to respond to community feedback as it constantly adds valued features Packt: You have a solid track record of developing software. What tools do you use on a day-to-day basis? GT: As a member of the Spring team, I regularly use IntelliJ IDEA, Slack, Gmail, Homebrew, various CI tools like CircleCI/TravisCI/Bamboo, Maven/Gradle, and Sublime Text 3. How to start using Spring Boot Packt: For a newbie developer, would you suggest getting started with Spring first, before trying their hand at Spring Boot? Or is Boot so simple to learn that a Java Developer could pick it up straight away and build applications? GT: In this day and age, there is no reason to not start with Spring Boot. The programming model is so simple and elegant that you can have a working web app in five minutes or less. And considering Spring Boot IS Spring, the argument is almost false. Packt: How does the new edition of Learning Spring Boot prepare readers to be industry-ready? For existing Spring and Spring Boot developers, what are the aspects to look forward to in your book? GT: This book contains a wide range of practical examples covering areas such as Web, Data Access, developer tools, Messaging, WebSockets, and Security. By building a realistic example throughout the book on top of Java's de facto standard toolkit, it should be easy to learn valuable lessons needed in today's industry. Additionally, using Project Reactor throughout, the reader will be ready to build truly scalable apps. As the Spring portfolio adopts support from Project Reactor, this is the only book in the entire market focused on that paradigm. Casting all these real world problems in light of such a powerful, scalable toolkit should be eagerly received. I truly believe this book helps bend the curve so that people can get operational, faster, and are able to meet their needs. How well does Spring Boot integrate with JavaScript and JavaScript frameworks? Packt: You also work a bit on JavaScript. Where do you think Spring and Spring Boot support for full-stack development with JS frameworks is going ahead? GT: Spring Boot provides first class support for either dropping in WebJars or self-compiled JavaScript modules, such as with Webpack. The fact that many shops are moving off of Ruby on Rails and onto Spring Boot is the evidence that Boot has it all needed to build strong, powerful apps with full blown front ends to meet the needs of development shops. What does the future hold for Spring Boot? Packt: Where do you see the future of Spring Boot's development going? What changes or improvements can the community expect in future releases? GT: Spring Boot has a dedicated team backing its efforts that at the same time is very respectful of community feedback. Adopting support for reactive programming is one such example that has been in motion for over two years. I think core things like the "Spring way" aren't going anywhere since they are all proven approaches. At the same time, support for an increasing number of 3rd party libraries and more cloud providers will be something to keep an eye on. Part of the excitement is not seeing exactly where things are going as well, so I look forward to the future of Spring Boot along with everyone else. Why you should read Learning Spring Boot Packt: Can you give Developers 3 reasons on why they should pick up your book? Are you interested in the hottest Java toolkit that is out there? Do you want to have fun building apps? And do you want to take a crack at the most revolutionary addition made to the Spring portfolio (Project Reactor)? If you answered yes to any of those, then this book is for you.

If NoSQL is the king, MongoDB is surely its crown jewel. With over 15 million downloads and counting, MongoDB is the most popular NoSQL database today, empowering users to query, manipulate and find interesting insights from their data. Alex Giamas is a Senior Software Engineer at the Department for International Trade, UK. Having worked as a consultant for various startups, he is an experienced professional in systems engineering, as well as NoSQL and Big Data technologies. Alex holds an M. Sc., from Carnegie Mellon University in Information Networking and has attended professional courses in Stanford University. He is a MongoDB-certified developer and a Cloudera-certified developer for Apache Hadoop & Data Science essentials. Alex has worked with a wide array of NoSQL and Big Data technologies, and built scalable and highly available distributed software systems in C++, Java, Ruby and Python. In this insightful interview with MongoDB expert Alex Giamas, we talk about all things related to MongoDB - from why NoSQL databases gained popularity to how MongoDB is making developers’ and data scientists’ work easier and faster. Alex also talks about his book Mastering MongoDB 3.x, and how it can equip you with the tools to become a MongoDB expert! Key Takeaways NoSQL databases have grown in popularity over the last decade because they allow users to query their data without having to learn and master SQL. The rise in popularity of the Javascript-based MEAN stack meant many programmers now prefer MongoDB as their choice of database. MongoDB has grown from being just a JSON data store to become the most popular NoSQL database solution with efficient data manipulation and administration capabilities. The sharding and aggregation framework, coupled with document validations, fine-grained locking, a mature ecosystem of tools and a vibrant community of users are some of the key reasons why MongoDB is the go-to database for many. Database schema design, data modeling, backup and security are some of the common challenges faced by database administrators today. Mastering MongoDB 3.x focuses on these common pain points of the database administrators and shows them how to build robust, scalable database solutions with ease. NoSQL databases seem to have taken the world by storm, and many people now choose various NoSQL database solutions over relational databases. What do you think is the reason for this rise in popularity? That's an excellent question. There are several factors contributing to the rise in popularity for NoSQL databases. Relational databases have served us for 30 years. At some point we realised that the one size fits all model is no longer applicable. While “software is eating the world” as Marc Andreessen has famously written, the diversity and breadth of use cases we use software for has brought an unprecedented specialisation in the level of solutions to our problems. Graph databases, column-based databases and of course document-oriented databases like MongoDB are in essence specialised solutions to particular database problems. If our problem fits the document-oriented use case, it makes more sense to use the right tool for the problem (e.g. MongoDB) than a generic one-size-fits-all RDBMS. Another contributing factor to the rise of NoSQL databases and especially MongoDB is the rise of the MEAN stack, which means Javascript developers can now work from frontend to backend and database. Last but not the least, more than a generation of developers have struggled with SQL and its several variations. The promise that one does not need to learn and master SQL to extract data from the database but can rather do it using Javascript or other more developer friendly tools is just too exciting to pass on. MongoDB struck gold in this aspect, as Javascript is one of the most commonly used programming languages. Using Javascript for querying also opened up database querying to the front end developers which I believe has driven adoption as well. MongoDB is one of the most popular NoSQL databases out there today, and finds application in web development as well as Big Data processing. How does MongoDB aid in effective analytics? In the past few years we have seen the explosive growth of generated data. 80% of the world’s data has been generated in the past 3 years and this will continue to happen even more in the near future with the rise of IoT. This data needs to be stored and most importantly analysed to derive insights and actions. The answer to this problem has been to separate the transactional loads from the analytical loads into OLTP and OLAP databases respectively. Hadoop ecosystem has several frameworks that can store and analyse data. The problem with Hadoop data warehouses/data lakes however is threefold. You need experts to analyse data, they are expensive and it’s difficult to get quickly the answers to your questions. MongoDB bridges this gap by offering efficient analytics capabilities. MongoDB can help developers and technical people get quick insights from data that can help define the direction of research for the data scientists working on the data lake. By utilising tools like the new charts or the BI connector, data warehousing and MongoDB are converging. MongoDB does not aim to substitute Hadoop-based systems but rather complement them and decrease the time to market for data-driven solutions. You have been using MongoDB since 2009, way back when it was in its 1.x version. How has the database has evolved over the years? When I started using MongoDB, it was not much more than a JSON data store. It’s amazing how far MongoDB has come in these 9 years in every aspect. Every piece of software has to evolve and adapt to the always changing environment. MongoDB started off as the JSON data store that is easy to setup and use while being blazingly fast with some caveats. The turning point for MongoDB early in its evolution was introducing sharding. Challenging as it may be to choose the right shard key, being able to horizontally scale using commodity hardware is the feature that has been appreciated the most by developers and architects throughout all these years. The introduction of aggregation framework was another turning point for MongoDB since it allowed developers to build data pipelines using MongoDB data, reducing time to market. Geospatial related features were there from an early point in time and actually one of MongoDB’s earliest and most visible customers, FourSquare was a vivid user of geospatial features in MongoDB. Overall, with time MongoDB has matured and is now a robust database for a wide set of use cases. Document validations, fine grained locking, a mature ecosystem of tools around it and a vibrant community means that no matter the language, state of development, startup or corporate environment, MongoDB can be evaluated as the database choice. There have been of course features and directions that didn’t end up as well as we were originally hoping for. A striking example is the MongoDB MapReduce framework which never lived up to the expectations of developers using MapReduce via Hadoop and has gradually been superseded by the more advanced and more developer-friendly Aggregation framework. What do you think are the most striking features of MongoDB? How does it help you in your day to day activities as a Senior Software Engineer? In my day to day development tasks I almost always use the Aggregation framework. It helps to quickly prototype a pipeline that can transform my data to a format that I can then collaborate with the data scientists to derive useful insights in a fraction of the time needed by traditional tools. Day to day or sprint to the next sprint - what you want from any technology is to be reliable and not get in your way but rather help you achieve the business goals. With MongoDB we can easily store data in JSON format, process it, analyse it and pass it on to different frontend or backend systems without much hassle. What are the different challenges that MongoDB developers and architects usually face while working with MongoDB? How does your book 'Mastering MongoDB 3.x' help, in this regard? The major challenge developers and architects face when choosing to work with MongoDB is the database design. Irrespective of whether we come from an RDBMS or a NoSQL background, designing the database such that it can solve our current and future problems is a difficult task. Having been there and struggled with it in the past, I have put emphasis on how someone coming from a relational background can model different relationships in MongoDB. I have also included easy to understand and follow checklists around different aspects of MongoDB. Backup and security is another challenge that users often face. Backups are many times ignored until it’s too late. In my book I identify all available options and the tradeoffs they come with, including cloud-based options. Security on the other hand is becoming an ever increasing concern for computing systems with data leaks and security breaches happening more often. I have put an emphasis on security both in the relevant chapters and also across most chapters by highlighting common security pitfalls and promoting secure practices wherever possible. MongoDB has commanded a significant market share in the NoSQL databases domain for quite some time now, highlighting its usefulness and viability in the community. That said, what are the 3 areas where MongoDB can get better, in order to stay ahead of its competition? MongoDB has conquered the NoSQL space in terms of popularity. The real question is how/if NoSQL can increase its market share in the overall database market. The most important area of improvement is interoperability. What developers get with popular RDBMS is not only the database engine itself, but also easy ways to integrate it with different systems, from programming frameworks to Big Data and analytics systems. MongoDB could invest heavier in building these libraries that can make a developer’s life easier. Real-time analytics is another area with huge potential in the near future. With IoT rapidly increasing the data volume, data analysts need to be able to quickly derive insights from data. MongoDB can introduce features to address this problem. Finally, MongoDB could improve by becoming more tunable in terms of the performance/consistency tradeoff. It’s probably a bit too much to ask from a NoSQL database to support transactions as this is not what it was designed to be from the very beginning, but it would greatly increase the breadth of use cases if we could sparingly link up different documents and treat them as one, even with severed performance degradation. Artificial Intelligence and Machine Learning are finding useful applications in every possible domain today. Although it's a database, do you foresee MongoDB going the Oracle way and incorporating features to make it AI-compatible? Throughout the past few years, algorithms, processing power and the sheer amount of data that we have available have brought a renewed trust in AI. It is true that we use ML algorithms in almost every problem domain, which is why every vendor is trying to make the developer’s life easier by making their products more AI-friendly. It’s only natural for MongoDB to do the same. I believe that not only MongoDB but every database vendor will have to gradually focus more on how to serve AI effectively, and this will become a key part of their strategy going ahead. Please tell us something more about your book 'Mastering MongoDB 3.x'. What are the 3 key takeaways for the readers? Are there any prerequisites to get the most out of the book? First of all, I would like to say that as a “Mastering” level book we assume that readers have some basic understanding of both MongoDB and programming in general. That being said, I encourage readers to start reading the book and try to pick up the missing parts along way. It’s better to challenge yourself than the other way around. As for the most important takeaways, in no specific order of importance: Know your problem. It’s important to understand and analyse as much as possible the problem that you are trying to solve. This will dictate everything, from data structures, indexing, to database design decisions, to technology choices. On the other hand, if the problem is not well defined then this may be the chance to shine for MongoDB as a database choice as we can store data with minimal hassle. Be ready to scale ahead of time. Whether that is replication or sharding, make sure that you have investigated and identified the correct design and implementation steps so that you can scale when needed. Trying to add an extra shard when load has already peaked in the existing shards is neither fun, nor easy to do. Use aggregation. Being able to transform data in MongoDB before extracting it for processing in an external database is a really important feature and should be used whenever possible, instead of querying large datasets and transforming their data in our application server. Finally, what advice would you give to beginners who would like to be an expert in using MongoDB? How would the learning path to mastering MongoDB look like? What are the key things to focus on in order to master data analytics using MongoDB? To become an expert in MongoDB, one should start by understanding its history and roots. They should understand and master schema design and data modelling. After mastering data modelling, the next step would be to master querying - both CRUD and more advanced concepts. Understanding the aggregation framework and how or when to index would be the next step. With this foundation, one can then move on to cross-cutting concerns like monitoring, backup and security, understanding the different storage engines that MongoDB supports and how to use MongoDB with Big Data. All this knowledge should then provide a strong foundation to move on to the scaling aspects like replication and sharding, with the goal of providing effective fault tolerance and high availability systems. Mastering MongoDB 3.x explains these topics in this order with the intention of getting from beginner to expert in a structured and easy to follow and understand way.

Data Science has popularly been dubbed as the sexiest job of the 21st century. So much so that everyone wants to become a data scientist. But what do you need to get started with data science? Do you need to have a degree in statistics? Why is having sound knowledge of statistics so important to be a good data scientist? We seek answers to these questions and look at data science through a statistical lens, in an interesting conversation with James D. Miller. [author title="James D. Miller"]James is an IBM certified expert and a creative innovator. He has over 35 years of experience in applications and system design & development across multiple platforms and technologies. Jim has also been responsible for managing and directing multiple resources in various management roles including project and team leader, lead developer and applications development director. He is the author or several popular books such as Big Data Visualization, Learning IBM Watson Analytics, Mastering Splunk, and many more. In addition, Jim has written a number of whitepapers and continues to write on a number of relevant topics based upon his personal experiences and industry best practices.[/author] In this interview, we look at some of the key challenges faced by many while transitioning from a data developer role to a data scientist. Jim talks about his new book, Statistics for Data Science and discusses how statistics plays a key role when it comes to finding unique, actionable insights from data in order to make crucial business decisions. Key Takeaways - Statistics for Data Science Data science attempts to uncover the hidden context of data by going beyond answering generic questions such as ‘what is happening’, to tackling questions such as ‘what should be done next’. Statistics for data science cultivates 'structured thinking' in one. For most data developers who are transitioning to the role of data scientist, the biggest challenge often comes in calibrating their thought process - from being data design-driven to more insight-driven Having a sound knowledge of statistics differentiates good data scientists from mediocre ones - it helps them accurately identify patterns in data that can potentially cause changes in outcomes Statistics for Data Science attempts to bridge the learning gap between database development and data science by implementing the statistical concepts and methodologies in R to build intuitive and accurate data models. These methodologies and their implementations are easily transferable to other popular programming languages such as Python. While many data science tasks are being automated these days using different tools and platforms, the statistical concepts and methodologies will continue to form their backbone. Investing in statistics for data science is worth every penny! Full Interview Everyone wants to learn data science today as it is one of the most in-demand skills out there. In order to be a good data scientist, having a strong foundation in statistics has become a necessity. Why do you think is this the case? What importance does statistics have in data science? With Statistics, it has always been about "explaining" (data). With data science, the objective is going beyond questions such as "what happened?" and the "what is happening?" to try to determine "what should be done next?". Understanding the fundamentals of statistics allows one to apply "structured thinking" to interpret knowledge and insights sourced from statistics. You are a seasoned professional in the field of Data Science with over 30 years of experience. We would like to know how your journey in Data Science began, and what changes you have observed in this domain over the 3 decades. I have been fortunate to have had a career that has traversed many platforms and technological trends (in fact over 37 years of diversified projects). Starting as a business applications and database developer, I have almost always worked for the office of finance. Typically, these experiences started with the collection - and then management of - data to be able to report results or assess performance. Over time, the industry has evolved and this work as becoming a “commodity” – with many mature tool options available and plenty of seasoned professionals available to perform the work. Businesses have now become keen to “do something more” with their data assets and are looking to move into the world of data science. The world before us offers enormous opportunities for those not only with a statistical background but someone with a business background that understands and can apply the statistical data sciences to identify new opportunities or competitive advantages. What are the key challenges involved in the transition from being a data developer to becoming a data scientist? How does the knowledge of statistics affect this transition? Does one need a degree in statistics before jumping into Data Science? Someone who has been working actively with data already has a “head start” in that they have experience with managing and manipulating data and data sources. They would also most likely have programming experience and possess the ability to apply logic to data. The challenge will be to “retool” their thinking from data developer to data scientist – for example, going from data querying to data mining. Happily, there is much that the data developer “already knows” about data science and my book Statistics for Data Science attempts to “point out” the skills and experiences that the data developer will recognize as the same or at least have significant similarities. You will find that the field of data science is still evolving and the definition of “data scientist” depends upon the industry, project or organization you are referring to. This means that there are many roles that may involve data science with each having perhaps quite different prerequisites (such as a statistical degree). You have authored a lot of books such as Big Data Visualization, Learning IBM Watson Analytics, etc. with the latest being Statistics for Data Science. Please tell us something about your latest book. The latest book, “Statistics for Data Science”, looks to point out the synergies between a data developer and data scientist and hopes to evolve the data developers thinking “beyond database structures”, but also introduces key concepts and terminologies such as probability, statistical inference, model fitting, classification, regression and more, that can be used to journey into statistics and data science. How is statistics used when it comes to cleaning and pre-processing the data? How does it help the analysis? What other tasks can these statistical techniques be used for? Simple examples of the use of statistics when cleaning and/or pre-processing of data (by a data developer) include data-typing, Min/Max limitation, addressing missing values and so on. A really good opportunity for the use of statistics in data or database development is while modeling data to design appropriate storage structures.  Using statistics in data development applies a methodical, structured approach to the process. The use of statistics can be a competitive advantage to any data development project. In the book, for practical purposes, you have shown the implementation of the different statistical techniques using the popular R programming language. Why do you think R is favored by the statisticians so much? What advantages does it offer? R is a powerful, feature-rich, extendable free language with many, many easy to use packages free for download. In addition, R has “a history” within the data science industry. R is also quite easy to learn and be productive with quickly. It also includes many graphics and other abilities “built-in”. Do you foresee a change in the way statistics for data science is used in the near future? In other words, will the dependency on statistical techniques for performing different data science tasks reduce? Statistics will continue to be important to data science. I do see more “automation” of more and more data science tasks through the availability of “off the shelf” packages that can be downloaded and installed and used. Also, the more popular tools will continue to incorporate statistical functions over time. This will allow for the main-streaming of statistics and data science into even more areas of life. The key will be for the user to have an understanding of the key statistical concepts and uses. What advice would you like to give to - 1 Those transitioning from the developer to the data scientist role, and 2. Absolute beginners, who want to take up statistics and data science as a career option? Buy my book! But seriously, keep reading and researching. Expose yourself to as much statistics and data science use cases and projects a possible. Most importantly, as you read about the topic, look for similarities between what you do today and what you are reading about. How does it relate? Always look for opportunities to use something that is new to you to do something you do routinely today. Your book 'Statistics for Data Science' highlights different statistical techniques for data analysis and finding unique insights from data. What are the three key takeaways for the readers, from this book? Again, I see (and point out in the book) key synergies between data or database development and data science. I would urge the reader – or anyone looking to move from data developer to data scientist - to learn through these and perhaps additional examples he or she may be able to find and leverage on their own. Using this technique, one can perhaps navigate laterally, rather than losing the time it would take to “start over” at the beginning (or bottom?) of the data science learning curve. Additionally, I would suggest to the reader that time taken to get acquainted with the R programs and the logic used for statistical computations (this book should be a good start) is time well spent.  

Data analysis plays a vital role in organizations today. It enables effective decision-making by addressing fundamental business questions based on the understanding of the available data. While there are tons of open source and enterprise tools for conducting data analysis, IBM SPSS Statistics has emerged as a popular tool among statistical analysts and researchers. It offers them the perfect platform to quickly perform data exploration and analysis, and share their findings with ease. [author title=""]  Dr. Kenneth Stehlik-Barry Kenneth joined SPSS as Manager of Training in 1980 after using SPSS for his own research for several years. He has used SPSS extensively to analyze and discover valuable patterns that can be used to address pertinent business issues. He received his PhD in Political Science from Northwestern University and currently teaches in the Masters of Science in Predictive Analytics program there. Anthony J. Babinec Anthony joined SPSS as a Statistician in 1978 after assisting Norman Nie, the founder of SPSS, at the University of Chicago. Anthony has led a business development effort to find products implementing technologies such as CHAID decision trees and neural networks. Anthony received his BA and MA in Sociology with a specialization in Advanced Statistics from the University of Chicago and is on the Board of Directors of the Chicago Chapter of the American Statistical Association, where he has served in different positions including the President. [/author] In this interview, we take a look at the world of statistical data analysis and see how IBM SPSS Statistics makes it easier to derive business sense from data. Kenneth and Anthony also walk us through their recently published book - Data Analysis with IBM SPSS Statistics - and tell us how it benefits aspiring data analysts and statistical researchers. Key Takeaways - IBM SPSS Statistics IBM SPSS Statistics is a key offering of IBM Analytics - providing an integrated interface for statistical analysis on-premise and on the cloud SPSS Statistics is a self-sufficient tool - it does not require you to have any knowledge of SQL or any other scripting language SPSS Statistics helps you avoid the 3 most common pitfalls in data analysis, i.e. handling missing data, choosing the best statistical method for analysis and understanding the results of the analysis R and Python are not direct competitors to SPSS Statistics - instead, you can create customized solutions by integrating SPSS Statistics with these tools for effective analyses and visualization Data Analysis with IBM SPSS Statistics highlights various popular statistical techniques to the readers, and how to use them in order to gather useful hidden insights from their data Full Interview IBM SPSS Statistics is a popular tool for efficient statistical analysis. What do you think are the 3 notable features of SPSS Statistics that make it stand apart from the other tools available out there? SPSS Statistics has a very short learning curve which makes it ideal for analysts to use efficiently. It also has a very comprehensive set of statistical capabilities so virtually everything a researcher would ever need is encompassed in a single application. Finally, SPSS Statistics provides a wealth of features for preparing and managing data so it is not necessary to master SQL or another database language to address data-related tasks. With over 20 years of experience in this field, you have a solid understanding of the subject and, equally, of SPSS Statistics. How do you use the tool in your work? How does it simplify your day to day tasks related to data analysis? I have used SPSS Statistics in my work with SPSS and IBM clients over the years. In addition, I use SPSS for my own research analysis. It allows me to make good use of my time whether I'm serving clients or doing my own analysis because of the breadth of capabilities available within this one program. The fact that SPSS produces presentation-ready output further simplifies things for me since I can collect key results as I work and put them into a draft report and share them as required. What are the prerequisites to use SPSS Statistics effectively? For someone who intends to use SPSS Statistics for their data analysis tasks, how steep is the curve when it comes to mastering the tool? It certainly helps to have a understanding of basic statistics when you begin to use SPSS Statistics but it can be a valuable tool even with a limited background in statistics. The learning curve is a very "gentle slope" when it comes to acquiring sufficient familiarity with SPSS Statistics to use it very effectively. Mastering the software does involve more time and effort but one can accomplish this over time as one builds on the initial knowledge that comes fairly easily. The good news is that one can obtain a lot of value from the software well before one truly masters it by discovering the many features.   What are some of the common problems in data analysis? How does this book help the readers overcome them? Some of the most common pitfalls encountered when analyzing data involve handling missing/incomplete data, deciding which statistical method(s) to employ and understanding the results. In the book, we go into the details of detecting and addressing data issues including missing data. We also describe what each statistical technique provides and when it is most appropriate to use each of them. There are numerous examples of SPSS Statistics output and how the results can be used to assess whether a meaningful pattern exists. In the context of all the above, how does your book Data Analysis with IBM SPSS Statistics help readers in their statistical analysis journey? What, according to you, are the 3 key takeaways for the readers from this book? The approach we took with our book was to share with readers the most straightforward ways to use SPSS Statistics to quickly obtain the results needed to effectively conduct data analysis. We did this by showing the best way to proceed when it comes to analyzing data and then showing how this process can be done best in the software. The key takeaways from our book are the way to approach the discovery process when analyzing data, how to find hidden patterns present in the data and what to look for in the results provided by the statistical techniques covered in the book.   IBM SPSS Statistics 25 was released recently. What are the major improvements or features introduced in this version? How do these features help the analysts and researchers? There are a lot of interesting new features introduced in SPSS Statistics 25. For starters, you can copy charts as Microsoft Graphic Objects, which allows you to manipulate charts in Microsoft Office. There are changes to the chart editor that make it easier to customize colors, borders, and grid line settings in charts. Most importantly, it allows the implementation of Bayesian statistical methods. Bayesian statistical methods enable the researcher to incorporate prior knowledge and assumptions about model parameters. This facility looks like a good teaching tool for Statistical Educators. Data visualization goes a long way in helping decision-makers get an accurate sense of their data. How does SPSS Statistics help them in this regard? Kenneth: Data visualization is very helpful when it comes to communicating findings to a broader audience and we spend time in the book describing when and how to create useful graphics to use for this purpose. Graphical examination of the data can also provide clues regarding data issues and hidden patterns that warrant deeper exploration. These topics are also covered in the book. Anthony: SPSS Statistics’ data visualizations capabilities are excellent. The menu system makes it easy to generate common chart types. You can develop customized looks and save them as a template to be applied to future charts. Underlying SPSS Graphics is an influential approach called the Grammar of Graphics. The SPSS graphics capabilities are embodied in a versatile syntax called Graphics Programming Language. Do you foresee SPSS Statistics facing stiff competition from open source alternatives in the near future? What is the current sentiment in the SPSS community regarding these topics? Kenneth: Open source tools based alternatives such as Python and R are potential competition for SPSS Statistics but I would argue otherwise. These tools, while powerful, have a much steeper learning curve and will prove difficult for subject matter experts that periodically need to analyze data. SPSS is ideally suited for these periodic analysts whose main expertise lies in their field which could be healthcare, law enforcement, education, human resources, marketing, etc. Anthony: The open source programs have a lot of capability but they are also fairly low-level languages, so you must learn to code. The learning curve is steep, and there are many maintainability issues. R has 2 major releases a year. You can have a situation where the data and commands remain the same, but the result changes when you update R. There are many dependencies among R packages. R has many contributors and is an avenue for getting your hands on new methods. However, there is a wide variance in the quality of the contributors and contributed packages. The occasional user of SPSS has an easier time jumping back in than does the occasional user of open source software. Most importantly, it is easier to employ SPSS in production settings. SPSS Statistics supports custom analytical solutions through integration with R and Python. Is this an intent from IBM to join hands with the open source community? This is a good follow-up question to the one asked before. Actually, the integration with R and Python allows SPSS Statistics to be extended to accommodate a situation in which an analyst wishes to try an algorithm or graphical technique not directly available in the software but which is supported in one of these languages. It also allows those familiar with R or Python to use SPSS Statistics as their platform and take advantage of all the built-in features it comes with, out of the box while still having the option to employ these other languages where they provide additional value. Lastly, this book is designed for analysts and researchers who want to get meaningful insights from their data as quickly as possible. How does this book help them in this regard? SPSS Statistics does make it possible to very quickly pull in data and get insightful results. This book is designed to streamline the steps involved in getting this done while also pointing out some of the less obvious "hidden gems" that we have discovered during the decades of using SPSS in virtually every possible situation.

Delivering Business Intelligence solutions to over 40000 customers worldwide, there is no doubt that Qlik has established a strong foothold in the analytics market for many years now. With the self-service capabilities of Qlik Sense, you can take better and more informed decisions than ever before. From simple data exploration to complex dashboarding and cloud-ready, multi-platform analytics, Qlik Sense gives you the power to find crucial, hidden insights from the depths of your data. We got some fascinating insights from our interview with two leading Qlik community members, Ganapati Hegde and Kaushik Solanki, on what Qlik Sense offers to its users and what the future looks like for the BI landscape. [box type="shadow" align="" class="" width=""] Ganapati Hegde Ganapati is an engineer by background and carries an overall IT experience of over 16 years. He is currently working with Predoole Analytics, an award-winning Qlik partner in India, in the presales role. He has worked on BI projects in several industry verticals and works closely with customers, helping them with their BI strategies. His experience in other aspects of IT, like application design and development, cloud computing, networking, and IT Security - helps him design perfect BI solutions. He also conducts workshops on various technologies to increase user awareness and drive their adoption. Kaushik Solanki Kaushik has been a Qlik MVP (Most Valuable Player) for the years 2016 and 2017 and has been working with the Qlik technology for more than 7 years now. An Information technology engineer by profession, he also holds a master’s degree in finance. Having started his career as a Qlik developer, Kaushik currently works with Predoole Analytics as the Qlik Project Delivery Manager and is also a certified QlikView administrator. An active member of Qlik community, his great understanding of project delivery - right from business requirement to final implementation, has helped many businesses take valuable business decisions.[/box] In this exciting interview, Ganapati and Kaushik take us through a compelling journey in self-service analytics, by talking about the rich features and functionalities offered by Qlik Sense. They also talk about their recently published book ‘Implementing Qlik Sense’ and what the readers can learn from it. Key Takeaways With many self-service and guided analytics features, Qlik Sense is perfectly tailored to business users Qlik Sense allows you to build customized BI solutions with an easy interface, good mobility, collaboration, focus on high performance and very good enterprise governance Built-in capabilities for creating its own warehouse, a strong ETL layer and a visualization layer for creating intuitive Business Intelligence solutions are some of the strengths of Qlik Sense With support for open APIs, the BI solutions built using Qlik Sense can be customized and integrated with other applications without any hassle. Qlik Sense is not a rival to Open Source technologies such as R and Python. Qlik Sense can be integrated with R or Python to perform effective predictive analytics ‘Implementing Qlik Sense’ allows you to upgrade your skill-set from a Qlik developer to a Qlik Consultant. The end goal of the book is to empower the readers to implement successful Business Intelligence solutions using Qlik Sense. Complete Interview There has been a significant rise in the adoption of Self-service Business Intelligence across many industries. What role do you think visualization plays in self-service BI? In a vast ocean of self-service tools, where do you think Qlik stands out from the others? As Qlik says visualization alone is not the answer. A strong backend engine is needed which is capable of strong data integration and associations. This then enables businesses to perform self-service and get answers to all their questions. Self-service plays an important role in the choice of visualization tools, as business users today no longer want to go to IT every time they need changes. Self service enable business users to quickly build their own visualization with simple drag and drop.   Qlik stands out from the rest in its capability to bring in multiple data sources, enabling users to easily answers questions. Its unique associative engine allows users to find hidden insights. The open API allows easy customization and integrations which is a must for enterprises. Data security and governance is one of the best in Qlik. What are the key differences between QlikView and Qlik Sense? What are the factors crucial to building powerful Business Intelligence solutions with Qlik Sense? QlikView and Qlik Sense are similar yet different. Both share the same engine. On one hand, QlikView is a developer’s delight with the options it offers, and on the other hand, Qlik Sense with its self-service is more suited for business users. Qlik Sense has better mobility and open API as compared to QlikView, making Qlik Sense more customizable and extensible. The beauty of Qlik Sense lies in its ability to help business get answers to their questions. It helps correlate the data between different data sources and making it very meaningful to users. Powerful data visualizations do not necessarily mean beautiful visualizations and Qlik Sense lays special emphasis on this. Finally what the users need is performance, easy interface, good mobility, collaboration and good enterprise governance - something which Qlik Sense provides. Ganapati, you have over 15 years of experience in IT, and have extensively worked in the BI domain for many years. Please tell us something about your journey. How does your daily schedule look like? I have been fortunate in my career to be able to work on multiple technologies ranging from programming, databases, information security, integrations and cloud solutions. All this knowledge is helping me propose the best solutions for my Qlik customers. It’s a pleasure helping customers in their analytical journey and working for a services company helps in meeting customers from multiple domains. The daily schedule involves doing Proof of Concepts/Demos for customers, designing optimum solutions on Qlik, and conducting requirement gathering workshops. It’s a pleasure facing new challenges every day and this helps me increase my knowledge base. Qlik open API opens up amazing new possibilities and lets me come up with out of the box solutions. Kaushik, you have been awarded the Qlik MVP for 2016 and 2017, and have experience of using Qlik's tools for over 7 years. Please tell us something about your journey in this field. How do you use the tool in your day to day work? I started my career by working with the Qlik technology. My hunger for learning Qlik made me addicted to the Qlik community. I learned lot many things from the community by asking questions and solving real-world problems of community members. This helped me to get awarded by Qlik as MVP for consecutively 2 years. MVP award motivated me to help Qlik customers and users and that is one of the reasons why I thought about writing a book on Qlik Sense. I have implemented Qlik not only for clients but also for my personal use cases. There are many ways in which Qlik helps me in my day-to-day work and makes my life much easier. It’s safe to say that I absolutely love Qlik. Your book 'Implementing Qlik Sense' is primarily divided into 4 sections - with each section catering to a specific need when it comes to building a solid BI solution. Could you please talk more about how you have structured the book, and why? BI projects are challenging, and it really hurts when a project doesn’t succeed. The purpose of the book is to enable Qlik Sense developers to get to implement successful Qlik Projects. There is often a lot of focus on development and thereby Qlik developers miss several other crucial factors which contribute to project success. To make the journey from a Qlik developer to a Qlik consultant the book is divided into 4 sections. The first section focuses on the initial preparation and intended to help consultant to get their groundwork done. The second section focuses on the execution of the project and intended to help consultants play a key role in rest of phases involving requirement gathering, architecture, design, development UAT. The third section is intended to make consultant familiar with some industry domains. This section is intended to help consultant in engaging better with business users and suggesting value-additions to project. The last section is to use the knowledge gained in the three sections and approaching a project with a case study which we come across routinely. Who is the primary target audience for this book? Are there any prerequisites they need to know before they start reading this book? The primary target audience is the Qlik Developers who are looking to progress in their career and are looking to wear the hat of a Qlik consultant.  The book is also for existing consultants who would like to sharpen their skills and use Qlik Sense more efficiently. The book will help them become trusted advisors to their clients. Those who are already familiar with some Qlik development will be able to get the most out of this book.   Qlik Sense is primarily an enterprise tool. With the rise of open source languages such as R and Python, why do you think people would still prefer enterprise tools for their data visualization? Qlik Sense is not a competition to R and Python but there are lots of synergies. The customer gets the best value when Qlik co-exists with R/Python and can leverage the capabilities of both Qlik and R/Python. Qlik Sense does not have the predictive capability which is easily fulfilled by R/Python. For the customer, the tight integration ensures he/she doesn’t have to leave the Qlik screen. There can be other use cases for using them jointly such as analyzing unstructured data and using machine learning. The reports and visualizations built using Qlik Sense can be viewed and ported across multiple platforms. Can you please share your views on this? How does it help the users? Qlik has opened all gates to integrate its reporting and visualization with most of the technologies through APIs. This has empowered customers to integrate Qlik with their existing portals and provide easy access to end users.  Qlik provides APIs for almost all its products, which makes Qlik the first choice for many CIOs because with those APIs they get a variety of options to integrate and automate their work. What are the other key functionalities of Qlik Sense that help the users build better BI solutions? Qlik Sense is not just a pure play data visualization tool. It has capabilities for creating its own warehouse, having an ETL layer and then of course there’s the visualization layer. For the customers, it’s all about getting all the relevant components required for their BI project in a single solution. Qlik is investing heavily in R&D and with its recent acquisitions and a strong portfolio, it is a complete solution enabling users to get all their use cases fulfilled. The open API has enabled opening newer avenues with custom visualizations, amazing concepts such as chatbots, augmented intelligence and much more. The core strength of strong data association, enterprise scalability, governance combined with all other aspects make Qlik one of the best in overall customer satisfaction. Do you foresee Qlik Sense competing strongly with major players such as Tableau and Power BI in the near future? Also, how do you think Qlik plans to tackle the rising popularity of the Open Source alternatives? Qlik has been classified as a Leader in the Gartner’s Magic Quadrant for several years now. We often come across Tableau and Microsoft Power BI as competition. We suggest our customers do a thorough evaluation and more often than not they choose Qlik for its features and the simplicity it offers. With recent acquisitions, Qlik Sense has now become an end-to-end solution for BI, covering uses cases ranging from report distributions, data-as-a-service, and geoanalytics as well. Open source alternatives have their own market and it makes more sense to leverage their capability rather than compete with them. An example, of course, is the strong integration of many BI tools with R or Python which makes life so much easier when it comes to finding useful insights from data. Lastly, what are the 3 key takeaways from your book 'Implementing Qlik Sense'? How will this book help the readers? The book is all about meeting your client’s expectations. The key takeaways are: Understand the role and  importance of Qlik consultant and why it’s crucial to be a trusted advisor to your clients Successfully navigating through all aspects which enable successful implementation of your Qlik BI Project. Focus on mitigating risks, driving adoption and avoiding common mistakes while using Qlik Sense. The book is ideal for Qlik developers who aspire to become Qlik consultants. The book uses simple language and gives examples to make the learning journey as simple as possible. It helps the consultants to give equal importance to certain phases of project development that often neglected. Ultimately, the book will enable Qlik consultants to deliver quality Qlik projects. If this interview has nudged you to explore Qlik Sense, make sure you check out our book Implementing Qlik Sense right away!

John Arundel is a DevOps consultant. Which means he helps businesses use software better. But when he's not supporting his clients, John is writing for Packt. John has written a number of books over the last few years, most recently Puppet 5 Beginner's Guide. Puppet is one of the most popular tools in the DevOp toolchain; it's a tool that gives administrators and architects significant control over their infrastructure. For that reason, it's a tool worth exploring - whatever field you're working in. It's likely to play a large part in the continued rise of DevOps throughout 2018. We spoke to John about Puppet, DevOps, and his new book - as well as his experience writing it. Packt: Your book is published now. How does it feel to be a published author? John Arundel: Pretty great! At one time I wrote technical manuals for Psion, the palmtop computer manufacturer. Thanks to a conservative house style, the kind of books I wrote said things like: “To access file operations, select the File menu”. Not exactly a page-turner. I’m very happy now to be able to publish a book which is written more or less exactly the way I want it, on a subject I find very interesting, and including a lot of jokes. What benefits did writing a book bring to your specialist area? JA: The funny thing is that despite being a Puppet user almost since the very beginning, I really don’t use many of its features. In fact, most of them have been added since I started using Puppet, and I don’t have a lot of time to experiment with new stuff, so writing the book was a great opportunity to delve into all the Puppet features I didn’t know about. I’m hoping that readers will also find out stuff they didn’t know and that will come in useful. If just one person is helped and inspired by this book... then I’m not giving refunds to the others. It’s done a lot to raise the profile of my consulting business; I was introduced to one potential client as “This is John. He literally wrote the book on Puppet”. I had to modestly point out that in fact, other, probably better books are available. Our authors usually have full-time jobs whilst writing for us. Was this the case for you and how did you approach managing your time? JA: As any freelancer knows, the job is more than full-time. I practically had to invent new physics to figure out a way of using my negative free time to write a book. I blocked out one day a week devoted to writing, and set myself a goal of a number of hours to achieve each month, which I mostly met. Because the book is so code-focused, I had to not only write about each technique I was describing, but also develop complete, working, reusable software in Puppet to implement it, and then test this on a virtual machine. Quite frequently I’d discover later that I’d been doing something wrong, or a behaviour in Puppet changed, and I’d have to go back and fix all the code. I’m sure there are still quite a few bugs, which I am going to pretend I’ve deliberately inserted to help the reader learn to debug and fix Puppet code: something they will, after all, spend a great deal of time doing. In all, what with researching, writing, coding, testing, fixing, editing, and complaining on Twitter, I spent about 200 hours on the book over 8 months. While writing your book, did you find that it overshadowed personal life in any way? How did you deal with this? JA: Not really. It could have, if I’d got into serious deadline trouble. Fortunately, I managed to keep up a continuous, manageable level of mild deadline trouble. I don’t think my friends or family noticed, except occasionally I’d say things at dinner like, “Could you pass the Puppet? I mean pepper.” Do you have any advice for other authors who may be interested in writing for Packt, but are still unsure? JA: Go for it! But make sure you have two hundred unallocated hours in your schedule. You’d be amazed how much time you can save by not watching TV, going out, putting on clothes, etc. Really, my advice would be to plan the book carefully - agreeing the outline in advance with your editor helps a lot. Henry Ford said that there are no big problems, just lots of little problems. Breaking down a book into chapters and sections and subsections and tackling them one by one makes it seem less daunting. And managing your time well helps avoid last-minute-essay syndrome. Do you have any tips for other authors, or tricks that you learnt whilst writing, that you'd like to share? JA: One good tip is, once you’ve written a chapter, let it lie fallow for a few weeks and then come back to it with a fresh eye. What you thought were immaculately-crafted sentences turn out to be pompous waffle. And what seemed clear and explicit now seems larded with techno-babble. I read somewhere that P.G. Wodehouse would stick each page of manuscript to the wall as he wrote it, somewhere around the skirting board level, and as he obsessively reworked and rewrote and polished the text he would gradually move it higher and higher up the wall until he judged it good enough - somewhere near the ceiling. Well, I’m not saying I’m P.G. Wodehouse — I’ll leave that for others to say — but it’s a useful way to think about the writing process. Rewrite, rewrite, rewrite! “What is written without effort,” Dr Johnson pointed out, “is in general read without pleasure.” Was there anything interesting that happened during the writing of the book? JA: Only in the sense that the Chinese use when they curse you to live in interesting times. Quite often I wrote myself to a standstill and just stared blankly at the laptop, hoping it would explain something complicated for me, or think up a useful and instructive example when I couldn’t. On one occasion I decided that the best thing to do with a certain long, difficult, and laboriously-constructed section was to delete it altogether, improving the book immeasurably as a result. The deleted scenes will be available on a forthcoming DVD, together with a ‘making of’ documentary which consists of me frowning at a screen for 200 hours and intermittently making tea. How did Packt’s Acquisition Editors help you - what kind of things did they help you with and how did they support you throughout the writing process? JA: The biggest help at the start was giving me structure by insisting on an outline, and then setting individual chapter deadlines to plan the writing time - then gently but persistently enforcing them. What was also very useful was to see sample chapters of other books, to get an idea of where I was supposed to be going, and getting very detailed feedback on the early chapters about exactly how to lay things out and how to make everything consistent. Beyond that, I was pleased and surprised by how little the editors interfered with what I was doing. By and large I was allowed to write my own book the way I wanted. No one suggested I write sentences like “To access file operations, select the File menu.” When I asked for help, I got it, and when I didn’t, I was left in peace and trusted to do the right thing. That’s a great way to write. What projects, if any, are you working on at the moment? Several people have asked what the next book’s going to be. I have said, only half-jokingly, that I might do one on Chef. I have a kind of a semi-formed idea about a book of system administration patterns and practices, based on my several decades worth of experience (read: mistakes). But just now I’m enjoying a break from writing, and I’m spending my negative free time reading other people’s books, playing Beethoven on my toy piano like Schroeder out of Peanuts, and learning to bake the perfect Cornish pasty. Ah! Excuse me, that was the oven timer. Thanks for taking the time to talk to us, John! You can find John's latest book, Puppet 5 Beginner's Guide, here.

The Industrial Internet, the IIoT, the 4th Industrial Revolution or Industry 4.0, whatever you may call it, has gained a lot of traction in recent times. Many leading companies are driving this revolution, connecting smart edge devices to cloud-based analysis platforms and solving their business challenges in new and smarter ways. To ensure the smooth integration of such machines and devices, effective architectural strategies based on accepted principles, best practices, and lessons learned, must be applied. In this interview, Shyam throws light on his new book, Architecting the Industrial Internet, and shares expert insights into the world of IIoT, Big Data, Artificial Intelligence and more. Shyam Nath Shyam is the director of technology integrations for Industrial IoT at GE Digital. His area of focus is building go-to-market solutions. His technical expertise lies in big data and analytics architecture and solutions with focus on IoT. He joined GE in Sep 2013 prior to which he has worked in IBM, Deloitte, Oracle, and Halliburton. He is the Founder/President of the BIWA Group, a global community of professional in Big Data, analytics, and IoT. He has often been listed as one of the top social media influencers for Industrial IoT. You can follow him on Twitter @ShyamVaran.   He talks about the IIoT, the various impacts that technologies like AI and Deep Learning will have on IIoT and he gives a futuristic direction to where IIoT is headed towards. He talks about the challenges that Architects face while architecting IIoT solutions and how his book will help them overcome such issues. Key Takeaways The fourth Industrial Revolution will break silos and bring IT and Ops teams together to function more smoothly. Choosing the right technology to work with involves taking risks and experimenting with custom solutions. The Predix platform and Predix.io allow developers and architects, quickly learn from others and build working prototypes that can be used to get quick feedback from the business users. Interoperability issues and a lack of understanding of all the security ramifications of the hyper-connected world could be a few challenges that adoption of IIoT must overcome Supporting technologies like AI, Deep Learning, AR and VR will have major impacts on the Industrial Internet In-depth Interview On the promise of a future with the Industrial Internet The 4th Industrial Revolution is evolving at a terrific pace. Can you highlight some of the most notable aspects of Industry 4.0? The Industrial Internet is the 4th Industrial Revolution. It will have a profound impact on both the industrial productivity as well as the future of work. Due to more reliable power, cleaner water, and Intelligent Cities, the standard of living will improve, at large for the world citizens. Industrial Internet will forge new collaborations between the IT and OT, in the organizations, and each side will develop a better appreciation of the problems and technologies of the other. They will work together to create smoother overall operations by breaking the silos. On Shyam’s IIoT toolbox that he uses on a day to day basis You have a solid track record of architecting IIoT applications in the Big Data space over the years. What tools do you use on a day-to-day basis? In order to build Industrial Internet applications, GE's Predix is my preferred IIoT platform. It is built for Digital Industrial solutions, with security and compliance baked into it. Customer IIoT solutions can be quickly built on Predix and extended with the services in the marketplace from the ecosystem. For Asset Health Monitoring and for reducing the downtime, Asset Performance Management (APM) can be used to get a jump start and its extensibility framework can be used to extend it. On how to begin one’s journey into building the Industry 4.0 For an IIoT architect, what would your recommended learning plan be? What aspects of architecting Industry 4.0 applications are tricky to master and how does your book Architecting the Industrial Internet, prepare its readers to be industry ready? An IIoT Architect can start with the book Architecting the Industrial Internet, to get a good grasp of the area broadly. This book provides a diverse set of perspectives and architectural principles, from authors who work in GE Digital, Oracle and Microsoft. The end-to-end IIoT applications involve an understanding of sensors, machines, control systems, connectivity and cloud or server systems, along with the understanding of associated enterprise data, the architect needs to focus on a limited solution or proof of concept first. The book provides coverage for the end-to-end requirements of the IIoT solutions for the architects, developers and business managers. The extensive set of use cases and case studies provides examples from many different industry domains to allow the readers to easily related to it. The book is written, in a style that would not overwhelm the reader, yet explain the workings of the architecture and the solutions. The book will be best suited for Enterprise Architects and Data Architects who are trying to understand how IIoT solutions differ from traditional IT solutions. The layer-by-layer description of the IIoT Architecture will provide a systematic approach to help develop a deep understanding, for Architects. IoT Developers who have some understanding of this area can learn the IIoT platform-based approach to building solutions quickly. On how to choose the best technology solution to optimize ROI There are so many IIoT technologies, that manufacturers are confused as to how to choose the best technology to obtain the best ROI. What would your advice to manufacturers be, in this regard? The manufacturers and operation leaders look for quick solutions to known issues, in a proven way. Hence, often they do not have the appetite to experiment with a custom solution, rather they like to know where the solution provider has solved similar problems and what was the outcome. The collection of use cases and case studies will help business leaders get an idea of the potential ROI while evaluating the solution. Getting to know Predix, GE’s IIoT platform, better Let's talk a bit about Predix, GE's IIoT platform. What advantages does Predix offer developers and architects? Do you foresee any major improvements coming to Predix in the near future? The GE's Predix platform has a growing developer community that is approaching 40,000 strong. Likewise, the ecosystem of Partners is approaching 1000. Coupled with the free access to create developer accounts on Predix.io, the developers and architects can quickly learn from others and build working prototypes that can be used to get quick feedback from the business users. The catalog of microservices at Predix.io will continue to expand. Likewise, applications written on top of Predix, such as APM and OPM (Operations Performance Management) will continue to become feature-rich, providing coverage to many common Digital Industrial challenges. On the impact of other emerging technologies like AI on IIoT What according to you will the impact be of AI and Deep Learning, on IIoT? AI and Deep Learning help to build robust Digtal Twins of the industrial assets. These Digital Twins, will make the job of predictive maintenance and optimization, much easier for the operators of these assets. Further, IIoT will benefit from many new advances in technologies like AI, AR/VR and make the job of Field Services Technicians easier. IIoT is already widely used in energy generation and distribution, Intelligent Cities for law enforcement and to ease traffic congestion. The field of healthcare is evolving, due to increasing use of wearables. Finally, precision agriculture is enabled by IoT as well. On likely barriers to IIoT adoption What are the roadblocks you expect in the adoption of IIoT? Today the challenges to rapid adoption of IoT, are interoperability issues and lack of understanding of all the security ramifications of the hyper-connected world. Finally, how to explain the business case of the IoT to the decision makers and different stakeholders is still evolving. On why Architecting the Industrial Internet is a must read for Architects Would you like to give architects 3 reasons on why they should pick up your book? It is written by IIoT practitioners from large companies who are building solutions for both internal and external consumption. The book captures the architectural best practices and advocates a platform based approach, to solutions. The theory is put to practice in the form of use cases and case studies, to provide a comprehensive guide to the architects. If you enjoyed this interview, do check out Shyam’s latest book, Architecting the Industrial Internet.

Analytics is slowly changing the face of the sports industry as we know it. Data-driven insights are being used to improve the team and individual performance, and to get that all-important edge over the competition. But what exactly is sports analytics? And how is it being used? What better way to get answers to these questions than asking an expert himself! [author title="Gaurav Sundararaman"]A Senior Stats Analyst at ESPN currently based in Bangalore, India. With over 10 years of experience in the field of analytics, Gaurav worked as a Research Analyst and a consultant in the initial phase of his career. He then ventured into sports analytics in 2012, and played a major role in the Analytics division of SportsMechanics India Pvt. Ltd. where he was the Analytics Consultant for the T20 World Cup winning West Indies team in 2016.[/author]   In this interview, Gaurav takes us through the current landscape of sports analytics, and talks about how analytics is empowering better decision-making in sports. Key Takeaways Sports analytics pertains to finding actionable, useful insights from sports data which teams can use to gain competitive advantage over the opposition Instincts backed by data make on and off-field decisions more powerful and accurate Rise of IoT and wearable technology has boosted sports analytics. With more data available for analysis, insights can be unique and very helpful Analytics is being used in sports right from improving player performance to optimizing ticket prices and understanding fan sentiments Knowledge of  tools for data collection, analysis and visualization such as R, Python and Tableau is essential for a sports analyst Thorough understanding of the sport, up to date skillset and strong communication with players and management are equally important factors to perform efficient analytics Adoption of analytics within sports has been slow, but steady. More and more teams are now realizing the benefits of sports analytics and are adopting an analytics-based strategy Complete Interview Analytics today is finding widespread applications in almost every industry today - how has the sports industry changed over the years? What role is analytics playing in this transformation? The sports industry has been relatively late in adopting analytics. That said, the use of analytics in sports has also varied geographically. In the west, analytics plays a big role in helping teams, as well as individual athletes, take up decisions. Better infrastructure and a quick adoption of the latest trends in technology is an important factor here. Also, investment in sports starts from a very young age in the west, which also makes a huge difference.  In contrast, many countries in Asia are still lagging behind when it comes to adopting analytics, and still leverage on traditional techniques to solve problems. A combination of analytics with traditional knowledge from experience would go a long way in helping teams, players and businesses succeed. Previously the sports industry was a very close community. Now with the advent of analytics, the industry has managed to expand its horizon. We witness more non-sportsmen playing a major part in the decision making. They understand the dynamics of the sports business and how to use data-driven insights to influence the same. Many major teams across different sports such as Football (Soccer), Cricket, American Football, Basketball and more have realized the value of data and analytics. How are they using it? What advantages does analytics offer to them? One thing I firmly believe is that analytics can’t replace skills or can’t guarantee wins. What it can do is ensure there is logic towards certain plans and decisions. Instincts backed by data make the decisions more powerful. I always tell the coaches or players – Go with your gut and instincts as Plan A. If it does not work out your fall back could be Plan B based on trends and patterns derived from data. It turns out to be a win-win for both. Analytics offers a neutral perspective which sometimes players or coaches may not realize. Each sport has a unique way of applying analytics to make decisions and obviously, as analysts, we need to understand the context and map the relevant data. As far as using the analytics is concerned, the goals are pretty straightforward - be the best, beat the opponents and aim for sustained success. Analytics helps you achieve each of these objectives. The rise of IoT and wearable technology over the last few years has been incredible. How has it affected sports, and sports analytics, in particular? It is great to see that many companies are investing in such technologies. It is important to identify where wearables and IoT can be used in sport and where it can cause maximum impact. These devices allow in-game monitoring of players, their performance, and their current physical state. Also, I believe more than on-field, these technologies would be very useful in engaging fans as well. Data derived from these devices could be used in broadcasting as well as providing a good experience for fans in the stadiums. This will encourage more and more people to watch games in stadiums and not in the comfort of their homes. We have already seen a beginning with a few stadiums around the world leveraging technology (IoT). The Mercedes Benz stadium (home of Atlanta Falcons) has a high tech stadium powered by IBM. Sacramento is building a state-of-the-art facility for the Sacramento Kings. This is just the start, and it will only get better with time. How does one become a sports analyst? Are there any particular courses/certifications that one needs to complete in order to become one? Can you share with us your journey in sports analytics? To be honest there are no professional courses yet in India to become an Analyst. There are a couple of colleges which have just started offering Sports Analytics as a course in their Post-Graduation Program. However, there are a few companies (Sports Mechanics and Kadamba Technologies in Chennai) that offer jobs that can enable you to become a Sports Analyst if you are really good.  If you are a freelancer then my advice would be to ensure you brand yourself well and showcase your knowledge through social media platforms and get a breakthrough via contacts. Post my MBA, Sports Mechanics (a leader in this space), a company based in Chennai were looking for someone to work to start their data practice. I was just lucky to be at the right place at the right time. I worked for 4 years there and was able to learn a lot about the industry and what works and what does not. Being a small company, I was lucky to don multiple hats and work on different projects across the value chain. I moved and joined the lovely team Of ESPNCricinfo where I work for their stats team. What are the tools and frameworks that you use for your day to day tasks? How do they make your work easier? There are no specific tools or frameworks. It depends on the enterprise you are working for. Usually, they are proprietary tools of the company. Most of these tools are used either to collect, mine or visualize data. Interpreting the information and presenting it in a manner in which users understand is important and that is where certain applications or frameworks are used. However to be ready for the future it would be good to be skilled on tools that support data collection, analysis and visualization namely R, Python and Tableau, to name a few. Do sports analysts have to interact with players and the coaching staff directly? How do you communicate your insights and findings with the relevant stakeholders? Yes, they have to interact with players and management directly. If not, the impact will be minimal. Communicating insights is very important in this industry. Too much analysis could lead to paralysis. We need to identify what exactly each player or coach is looking for, based on their game and try to provide them the information in a crisp manner which helps them make decisions on and off the field. For each stakeholder the magnitude of the information provided is different. For the coach and management, the insights can be in detail while for the players we need to keep it short and to the point. The insights you generate must not only be limited to enhancing the performance of a team on the field but much more than that. Could you give us some examples? Insights can vary. For the management, it could deal with how to maximise the revenue or save some money in an auction. For coaches, it could help them know about his team’s as well as the opposition’s strengths and weaknesses from a different perspective. For captains, data could help in identifying some key strategies on the field. For example, in Cricket, it could help the captain determine which bowler to bring on to which opposition batsmen, or where to place the fielders. Off the field, one area where analytics could play a big role would be in grassroots development and tracking of an athlete from an early age to ensure he is prepared for the biggest stage. Monitoring performance, improving physical attributes by following a specific regimen, assessing injury record and designing specific training programs, etc. are some ways in which this could be done. What are some of the other challenges that you face in your day to day work? Growth in this industry can be slow sometimes. You need to be very patient, work hard and ensure you follow the sport very closely. There are not many analytical obstacles as such, but understanding the requirements and what exactly the data needs are can be quite a challenge. Despite all the buzz, there are quite a few sports teams and organizations who are still reluctant to adopt an analytics-based strategy – why do you think is that the case? What needs to change? The reason for the slow adoption could be the lack of successful case studies and the awareness. In most sports when so many decisions are taken on the field sometimes the players' ability and skill seems far more superior to anything else. As more instances of successful execution of data-based trends come up, we are likely to see more teams adopting the data-based strategy. Like I mentioned earlier, analytics needs to be used to make the coach and captain take the most logical and informed decisions. Decision-makers need to be aware of the way it is used and how much impact it can cause.  This awareness is vital towards increasing the adoption of analytics in sports. Where do you see sports analytics in the next 5-10 years? Today in sports many decisions are taken on gut feeling, and I believe there should be a balance. That is where analytics can help. In sports like Cricket, only around 30% of the data is used and there is more emphasis given to video. Meanwhile, if we look at Soccer or Basketball, the usage of data and video analytics is close to 60-70% of its potential. Through awareness and trying out new plans based on data, we can increase usage of analytics in cricket to 60-70 % in the next few years. Despite the current shortcomings, It is fair to say that there is a progressive and positive change at the grassroots level across the world. Data-based coaching and access to technology are slowly being made available to teams as well as budding sportsmen/women. Another positive is that the investment in the sports industry is growing steadily. I am confident that in a couple of years, we will see more job opportunities in sports. Maybe in five years, the entire ecosystem would be more structured and professional. We would witness analytics playing a much bigger role in helping stakeholders make informed decisions, as data-based insights become even more crucial. Lastly, what advice do you have for aspiring sports analysts? My only advice would be - Be passionate, build a strong network of people around you, and constantly be on the lookout for opportunities. Also, it is important to keep updating your skill-set in terms of the tools and techniques needed to perform efficient and faster analytics. Newer and better tools keep coming up very quickly, which make your work easier and faster. Be on the lookout for such tools! One also needs to identify their own niche based on their strengths and try to build on that. The industry is on the cusp of growth and as budding analysts, we need to be prepared to take off when the industry matures. Build your brand and talk to more people in the industry - figure out what you want to do to keep yourself in the best position to grow with the industry.

IBM’s SPSS Modeler provides a powerful, versatile workbench that allows you to build efficient and accurate predictive models in no time. What else separates IBM SPSS Modeler from other enterprise analytics tools out there today? To know just that, we talk to arguably two of the most popular members of the SPSS community. [box type="shadow" align="" class="" width=""] Keith McCormick Keith is a career-long practitioner of predictive analytics and data science, has been engaged in statistical modeling, data mining, and mentoring others in this area for more than 20 years. He is also a consultant, an established author, and a speaker. Although his consulting work is not restricted to any one tool, his writing and speaking have made him particularly well known in the IBM SPSS Statistics and IBM SPSS Modeler communities. Jesus Salcedo Jesus is an independent statistical consultant and has been using SPSS products for over 20 years. With a Ph.D., in Psychometrics from Fordham University, he is a former SPSS Curriculum Team Lead and Senior Education Specialist, and has developed numerous SPSS learning courses and trained thousands of users.[/box] In this interview with Packt, Keith and Jesus give us more insights on the Modeler as a tool, the different functionalities it offers, and how to get the most out of it for all your data mining and analytics needs. Key Interview Takeaways IBM SPSS Modeler is easy to get started with but can be a tricky tool to master Knowing your business, your dataset and what algorithms you are going to apply are some key factors to consider before building your analytics solution with SPSS Modeler SPSS Modeler’s scripting language is Python, and the tool has support for running R code IBM SPSS Modeler Essentials helps you effectively learn data mining and analytics, with a focus on working with data than on coding Full Interview Predictive Analytics has garnered a lot of attention of late, and adopting an analytics-based strategy has become the norm for many businesses. Why do you think this is the case?   Jesus: I think this is happening because everyone wants to make better-informed decisions.  Additionally, predictive analytics brings the added benefit of discovering new relationships that you were previously not aware of. Keith: That’s true, but it’s even more exciting when the models are deployed and are potentially driving automated decisions. With over 40 years of combined experience in this field, you are master consultants and trainers, with an unrivaled expertise when it comes to using the IBM SPSS products. Please share with us the story of your journey in this field. Our readers would also love to know how your day-to-day schedule looks like.   Jesus: When I was in college, I had no idea what I wanted to be. I took courses in many areas, however I avoided statistics because I thought it would be a waste of time, after all, what else is there to learn other than calculating a mean and plugging it into fancy formulas (as a kid I loved baseball, so I was very familiar with how to calculate various baseball statistics). Anyway, I took my first statistics course (where I learned SPSS) since it was a requirement, and I loved it. Soon after I became a teaching assistant for more advanced statistics courses and I eventually earned my Ph.D. in Psychometrics, all the while doing statistical consulting on the side. After graduate school, my first job was as an education consultant for SPSS (where I met Keith). I worked at SPSS (and later IBM) for seven years, at first focusing on training customers on statistics and data-mining, and then later on developing course materials for our trainings. In 2013 Keith invited me to join him as an IBM partner, so we both trained customers and developed a lot of new and exciting material in both book and video formats. Currently, I work as an independent statistical and data-mining consultant and my daily projects range from analyzing data for customers, training customers so they can analyze their own data, or creating books and videos on statistics and data mining. Keith: Our careers have lots of similarities. My current day to day is similar too. Lately, about 1/3rd of my year is lecturing and curriculum development for organizations like TDWI (Transforming Data with Intelligence), The Modeling Agency, and UC Irvine Extension. The majority of my work is in predictive analytics consulting. I especially enjoy projects where I’m brought in early and can help with strategy and planning. Then, the coach and mentor take over a team until they are self-sufficient. Sometimes building the team is even more exciting than the first project because I know that they will be able to do many more projects in the future. There is a plethora of predictive analytics tools used today - for desktop and enterprises. IBM SPSS Modeler is one such tool. What advantages does SPSS Modeler have over the others, in your opinion? Keith: One of our good friends who co-authored the IBM SPSS Modeler Cookbook made an interesting comment about this at a conference. He is unique in that he has done one-day seminars using several different software tools. As you know, it is difficult to present data mining in just one day. He said that only with Modeler he is able to spend some time on each of the CRISP-DM phases of a case study in a day. I think he feels this way because it’s among the easiest options to use. We agree. While powerful, and while it takes a whole career to master everything, it is easy to get started. Are there any prerequisites for using SPSS Modeler? How steep is the learning curve in order to start using the tool effectively? Keith: Well, the first thing I want to mention is that there are no prerequisites for our PACKT video IBM SPSS Modeler Essentials. In that, we assume that you are starting from scratch. For the tool in general, there aren’t any specific requisites as such, however knowing your data, and what insights you are looking for always helps. Jesus: Once you are back at the office, in order to be successful on a data mining project or efficiently utilize the tool, you’ll need to know your business, your data, and the modeling algorithm you are using. Keith: The other question that we get all the time is how much statistics and machine learning do you have to know. Our advice is to start with one or maybe two algorithms and learn them well. Try to stick to algorithms that you know. In our PACKT course, we mostly focus on just Decision Trees, which one of the easiest to learn. What do you think are the 3 key takeaways from your course - IBM SPSS Modeler Essentials? The 3 key takeaways from this course, we feel are: Start slow. Don’t pressure yourself to learn everything all at once. There are dozens of “nodes” in Modeler. We introduce the most important ones so start there. Be brilliant in the basics. Get comfortable with the software environment. We recommend the bests ways to organize your work. Don’t rush to Modeling. Remember the Cross Industry Standard Process for Data Mining (CRISP-DM), which we cover in the video. Use it to make sure that you proceed systematically and don’t skip critical steps. IBM recently announced that SPSS Modeler would be available freely for educational usage. How can one make the most of this opportunity? Jesus: A large portion of the work that we have done over the past few years has been to train people on how to analyze data. Professors are in a unique position to expose more students to data mining since we teach only those students whose work requires this type of training, whereas professors can expose a much larger group of people to data mining. IBM offers several programs that support professors, students, and faculty; for more information visit: https://www-01.ibm.com/software/analytics/spss/academic/ Keith: When seeking out a university class, whether it be classroom or online, ask them if they use Modeler or if they allow you to complete your homework assignments in Modeler. We recognize that R based classes are very popular now, but you potentially won’t learn as much about Data Mining. Sometimes too much of the class is spent on coding so you learn R, but learn less about analytics. You want to spend most of the class time actively working with data and producing results. With the rise of open source languages such as R and Python and their applications in predictive analytics, how do you foresee enterprise tools like SPSS Modeler competing with them? Keith: Perhaps surprisingly, we don’t think Modeler does compete with R or Python. A lot of folks don’t know that Python is Modeler’s scripting language. Now, that is an advanced feature, and we don’t cover it in the Essentials video, but learning Python actually increases your knowledge of Modeler. And Modeler supports running R code right in a Modeler stream by using the R nodes. So Modeler power users (or future power users) should keep learning R on their to-do list. If you prefer not to use code, you can produce powerful results without learning either by just using Modeler straight out of the box. So, it really is all up to you. If this interview has sparked your interest in learning more about IBM SPSS Modeler, make sure you check out our video course IBM SPSS Modeler Essentials right away!

Max Kanat Alexander understands software implicitly. He has spent his career not only working with it, but thinking about it too. But don't think for a moment that Max is an armchair philosopher. His philosophy has real-world consequences for all programmers, offering a way to write better code and achieve incredible results while remaining happy and healthy in an industry that can be exhausting.  In his new book Understanding Software Max explores a wide range of issues that should matter to anyone working with software - we spoke to him about it, and discussed his thoughts on how thinking about software can help us all.  You're currently working at Google. Could you tell us what that's like and what you're working on at the moment? Max Kanat-Alexander: I can't answer this question in a public setting without approval from Google PR. However, there is a public blog post that describes what I do and provides my title. Why simplicity is so important in software Your last book was called “Code Simplicity” – could you tell us exactly what that means and why you think it’s so important today? MKA: One of the things that I go over in that book, and that I cover also in my new book, is that most of the problems of software fundamentally come from code complexity. Even when it doesn't seem like they do, if you trace down most problems far enough, you'll see that they never would have happened if there hadn't been so much code complexity. This isn't obvious to everybody, though, so I wrote a book that provides a long reasoned argument that explains (a) the fundamental laws of software design and (b) hopefully brings the reader to understanding why simplicity (and maintaining that simplicity) is so important for software. I figured that one of the primary causes of complexity was simply the lack of full and complete understanding by every programmer of what complexity really is, where it comes from, why it's important, and what the simple steps are that you take to handle it. And even now, when I go back to the book myself, I'm always surprised that there are so many answers to the problems I'm facing now. When you've discovered the fundamental laws of software development, it turns out that they do in fact resolve problems even long after their discovery. Do you think you can approach code the same way whatever languages or software you’re using? Is there a philosophy you think any engineer can adopt? Or is flexibility key? MKA: There are fundamental laws and principles that are true across any language. Fundamentally, a language is a way of representing a concept that the programmer has and wants to communicate to a computer. So there are ways of structuring concepts, and then there are ways of structuring things in a language. These both have rules, but the rules and principles of structuring concepts are the senior principles over the rules for structuring things in a language, because the rules for how you organize or handle a concept apply across any language, any computer, any set of tools, etc. Theoretically, there should be an ideal way to represent any particular set of concepts, but I'm not sure that any of our languages are there yet. The philosophy I've expressed in Code Simplicity and now in Understanding Software is entirely a universal philosophy that applies to all software development. I don't generally write about something if you can only use it in one language or with one framework. There is enough of that sort of writing out in the world, and while it's really valuable, I don't feel like it's the most valuable contribution that I personally have to bring to the world of software development. Since I have a background in some types of philosophy as well as a lot of experience doing design (and extensive refactoring) across many languages on many large projects, there's a universal viewpoint that I've tried to bring and that a lot of people have told me has helped them. To answer your last question, when you say the word "flexibility," you're in dangerous territory, because a lot of people interpret that as meaning that you should write endless generic code up front even if that doesn't address the immediate requirements of your system. This leads to a lot of code complexity, particularly in larger systems, so it's not a good idea. But some people interpret the word "flexibility" that way. For a more nuanced view, you kind of have to read all of Code Simplicity and then see some of the newer content in Understanding Software, too. Are there any languages where code simplicity is particularly important? Or any in which it is challenging to keep things simple? MKA: More than for a particular language, I would say it's important for the size and longevity of a project. Like, if I'm writing a one-off script that I'm only going to use for the next five minutes, I'll take a lot of shortcuts. But if I'm working on a multi-million line codebase that is used by millions of people, simplicity is of paramount importance. There are definitely languages in which it's more challenging to keep things simple. I've made this statement before, and there are a lot of amazing developments in the Perl community since I first made it, but it was a lot of work to keep the Bugzilla Project's Perl codebase healthy when I worked on it--more so than with other languages. Some of that had to do with the design of that particular codebase, but also that Perl allows you to accomplish the same thing in so many different ways. It led to a lot more thorough and time-consuming code reviews where we would frequently have to tell people to do things in the way that our codebase does them, or the particular way that we'd learned was the most maintainable through hard-earned experience. It did still result in a well-designed codebase, and you can write well-designed Perl, but it required more language experience and more attention in code review than I've experienced when writing in other languages. Bash is similar in some ways--I once maintained a several-thousand-line Bash program, and that was pretty challenging in terms of simplicity.  Languages aren't equal--some are better than others for some types of tasks. There are tasks for which I love Perl and Bash, and others for which Java, Python, or Go are definitely more suitable. Some of this has to do with the suitability of a particular tool to a particular task, but more of it has to do with things like how much structure the language allows you to place around your code, how much consistency the language libraries have, etc. What makes code bad? What, in your opinion, makes code ‘bad’? And how can we (as engineers and developers) identify it? MKA: It's bad if it's hard to read, understand, or modify. That's the definition of "complex" for code. There's a lot to elaborate on there, but that's the basic idea. What do you think the main causes of ‘bad code’ are? Lack of understanding on the part of the programmer working on the system. A failure of the programmer to take sufficient responsibility for enough of the system. A simple unawareness of the problem. On your blog you talk a lot about how code is primarily ‘human’. Could you elaborate on that and what it means for anyone that works in software?  Sure. It’s people that write software, people that read it, and people that use it. The whole purpose of software is actually to help people, not to help computers or network cables. When you're looking at solving the problems of software development, you have to look at it as a human discipline--something that people do, something that has to do with the mind or the being or the perceptions of individuals. Even though that might sound a bit wishy-washy, it's absolutely true. If you think of it purely as being about the computer, you end up writing compilers and performance tools (which are great) but you don't solve the actual problems that programmers have. Because it's the programmers or the users who have problems--not the machines, for the most part. You have to talk to people, find out what's going on with them. Just as one example, one of the best ways to detect complexity is to ask people for negative emotional reactions to code. "What part of this codebase is the most frustrating?" "What part of this codebase are you the most afraid to touch because you think you'll break something?" Questions like that. And those are basically questions about people, even though you're getting data about code. Because when you come down to it, the whole reason you're getting data about code has something to do with people. You can't lose sight of the fact that the problem you're resolving is code complexity, but at the same time, you also can't forget the reason you're resolving it, which is to help programmers. How has your thinking around code changed over the years? Are there any experiences that stand out as important in shaping your philosophy today? MKA: I've always been fairly meticulous in terms of how I want to approach code. However, it's also important that that meticulousness delivers a product. There have been times, particularly in my early coding career, when I would go to clean up some codebase or improve something only to find that nobody actually wanted the end result. That is, you can work on a project for a long time that nobody actually ends up using or caring about. So one of the lessons that I learned, which is expressed in both Code Simplicity and Understanding Software, is that your software has to actually help somebody or you're not going to actually end up being very happy with it, even if you enjoy the process of working on it. Also, the more time I spend working with groups of engineers as opposed to working alone or in distributed teams, the more I learn about how to communicate the basic principles of software development to other engineers. I think that Code Simplicity did a pretty good job, but weirdly, sometimes you can be too simple for some people. That is, if you get too fundamental with your explanations, then sometimes people don't see how to use the information or how it applies to their life. Sometimes you have to get a bit more complex than the fundamentals, explain a bit more about how to use the data or how it might apply in some specific situation, because some programmers are very focused on their specific problem in present time. That is, they don't want to hear about how to solve all problems like the one they're having - they can't even listen to that or really digest it. So instead you have to frame your explanation in terms of how it applies to this specific problem, which--when you're coming from the fundamental laws of software design all the way up to some extreme complexity that some programmer has boxed themselves into--can become quite difficult to fully communicate. It's also tough because they often think they already know the fundamentals, even though the problem they're having clearly indicates that they don't. So you have to learn how to communicate around those barriers. Working on the Bugzilla Project was a significant influence in how I think about software development, because I proved that you really can refactor a legacy codebase that's gotten into a very bad or very complex state, and that that has significant effects on the product. I think that's one of the things that's been missing in other attempts or writings, is that there's a lot of talk about how code quality is important, but I (and the people who worked with me on Bugzilla) actually went through the process of making significant impacts on products and users over a period of many years by focusing almost purely on code quality and applying the basic principles of software design. There have been lots of other experiences that have been an influence. I think that I generally try to learn something from every encounter I have with programming or software engineers. It's dangerous to think that you already know everything already--then you stop learning. 3 things to take away from Understanding Software What are the three things you want people to take away from Understanding Software? Well, I hope that they take at least as many things away from the book as there are chapters in the book. But in general, I'd like people to have some good ideas about how to handle problems that they run into in terms of code complexity. I'd also like them to understand more about the fundamental reasons that we handle code complexity, as well as the rules around it. I'd like people to see the basic philosophy of software development as something that they can think with themselves, not just something that somebody else has said, and I'd like them to be able to use that basic philosophy to resolve the problems of their software development environments. And of course, I'd like people to come away from the book being much better and faster programmers than they were before.  3 ways engineers can be more productive What 3 tips can you give to developers and engineers who want to be more productive?   1. Understand as much as you can about software, the project you're working on, etc. See the chapter "Why Programmers Suck" in Understanding Software for all the details on this and other things that you should learn about.   2. Try to take responsibility beyond the normal bounds of your code. That is, if you're normally only responsible for one tiny part of the system, also start thinking about how you could improve the parts of the system that talk to your part. Think about the consumers of your code and work to refactor them as well, when necessary. Keep expanding your sphere of responsibility over your codebase over time, even to the point of helping out with the tools and languages that you use to develop software, eventually.   3. Stop thinking so much about your code and start coding more. Or do drawings, or take a walk, or talk to somebody, or something. See the chapter "Stop Thinking: The Secret of Fast Programming" in Understanding Software for the full details on this.

[dropcap]S[/dropcap]elf-service Business Intelligence is the buzzword everyone's talking about today. It gives modern business users the ability to find unique insights from their data without any hassle. Amidst a myriad of BI tools and platforms out there in the market, Microsoft’s Power BI has emerged as a powerful, all-encompassing BI solution - empowering users to tailor and manage Business Intelligence to suit their unique needs and scenarios. [author title="Brett Powell"]A Microsoft Power BI partner, and the founder and owner of Frontline Analytics LLC., a BI and analytics research and consulting firm. Brett has contributed to the design and development of Microsoft BI stack and Power BI solutions of diverse scale and complexity across the retail, manufacturing, financial, and services industries. He regularly blogs about the latest happenings in Microsoft BI and Power BI features at Insight Quest. He is also an organizer of the Boston BI User Group.[/author]   In this two part interview Brett talks about his new book, Microsoft Power BI Cookbook, and shares his insights and expertise in the area of BI and data analytics with a partciular focus on Power BI. In part one of the interview, Brett shared his views on topics ranging from what it takes to be successful in the field of BI & data analytics to why he thinks Microsoft is going to lead the way in shaping the future of the BI landscape. Today in part two, he shares his expertise with us on the unique features that differentiate Power BI from other tools and platforms in the BI space. Key Takeaways Ease of deployment across multiple platforms, efficient data-driven insights, ease of use and support for a data-driven corporate culture is what defines an ideal Business Intelligence solution for enterprises. Power BI leads in self-service BI because it’s the first Software as a Service (SaaS) platform to offer ‘End User BI’ in which anyone, not just a business analyst, can leverage powerful tools to obtain greater value from data. Microsoft Power BI has been identified as a leader in Gartner’s Magic Quadrant for BI and Analytics platforms, and provides a visually rich and easy to access interface that modern business users require. You can isolate report authoring from dataset development in Power BI, or quickly scale up or down a Power BI dataset as per your needs. Power BI is much more than just a tool for reports and dashboards. With a thorough understanding of the query and analytical engines of Power BI, users can customize more powerful and sustainable BI solutions. Part Two: Interview Excerpts - Power BI from a Worm’s Eye View How long have you been a Microsoft Power BI user? How have you been using Power BI on a day-to-day basis? What other tools do you generally end up using alongside Power BI for your work? I’ve been using Power BI from the beginning when it was merely an add-in for Excel 2010. Back then, there was no cloud service and Microsoft BI was significantly tethered to SharePoint but the fundamentals of the Tabular data modelling engine and programming language of DAX was available in Excel to build personal and team solutions. On a day-to-day basis I regularly work with Power BI datasets – that is, the analytical data models inside of Power BI Desktop files. I also work with Power BI report authoring and visualization features and with various data sources for Power BI such as SQL Server. From Learning to Mastering Power BI For someone just starting out using Power BI, what would your recommended learning plan be? For existing users, what does the road to mastering Microsoft Power BI look like? When you’re just starting out I’d recommend learning the essentials of the Power BI architecture and how the components (Power BI service, Power BI Desktop, On-Premises Data Gateway, Power BI Mobile, etc) work together. A sound knowledge on the differences between datasets, reports, and dashboards is essential and an understanding of app workspaces and apps is strongly recommended as this is the future of Power BI content management and distribution. In terms of a learning path you should consider what your role will be on Power BI projects – will you be administering Power BI, creating reports and dashboards, or building and managing datasets? Each of these roles has their own skills, technologies and processes to learn. For example, if you’re going to be designing datasets, a solid understanding of the DAX language and filter context is essential and knowledge of M queries and data access is very important as well. The road to mastering Power BI, in my view, involves a deep understanding of both the M and DAX languages in addition to knowledge of Power BI’s content management, delivery, and administration processes and features. You need to be able to contribute to the full lifecycle of Power BI projects and help guide the adoption of Power BI across an organization. The most difficult or ‘tricky’ aspect of Power BI is thinking of M and DAX functions and patterns in the context of DirectQuery and Import mode datasets. For example, certain code or design patterns which are perfectly appropriate for import models are not suitable for DirectQuery models. A deep understanding of the tradeoffs and use cases for DirectQuery versus default Import (in-memory) mode and the ability to design datasets accordingly is a top characteristic of a Power BI master. 5+ interesting things (you probably didn’t know) about Power BI What are some things that users may not have known about Power BI or what it could do? Can readers look forward to learning to do some of them from your upcoming book: Microsoft Power BI Cookbook? The great majority of learning tutorials and documentation on Power BI involves the graphical interfaces that help you get started with Power BI. Likewise, when most people think of Power BI they almost exclusively think of data visualizations in reports and dashboards – they don’t think of the data layer. While these features are great and professional Power BI developers can take advantage of them, the more powerful and sustainable Power BI solutions require some level of customization and can only be delivered via knowledge of the query and analytical engines of Power BI. Readers of the Power BI Cookbook can look forward to a broad mix of relatively simple to implement tips on usability such as providing an intuitive Fields list for users to more complex yet powerful examples of data transformations, embedded analytics, and dynamic filter behaviours such as with Row-level security models. Each chapter contains granular details on core Power BI features but also highlights synergies available by integrating features within a solution such as taking advantage of an M query expression, a SQL statement, or a DAX metric in the context of a report or dashboard. What are the 3 most striking features that make you love to work with Power BI? What are 3 aspects you would like improved? The most striking feature for me is the ability to isolate report authoring from dataset development. With Power BI you can easily implement a change to a dataset such as a new metric and many report authors can then leverage that change in their visualizations and dashboards as their reports are connected to the published version of the dataset in the Power BI service. A second striking feature is the ‘Query Folding’ of the M query engine. I can write or enhance an M query such that a SQL statement is generated to take advantage of the data source system’s query processing resources. A third striking feature is the ability to quickly scale up or down a Power BI dataset via the dedicated hardware available with Power BI Premium. With Power BI Premium, free users (users without a Pro License) are now able to access Power BI reports and dashboards. The three aspects I’d like to see improved include the following: Currently we don’t have IntelliSense and other common development features when writing M queries. Currently we don’t have display folders for Power BI datasets thus we have to work around this with larger, more complex datasets to maintain a simple user interface. Currently we don’t have Perspectives, a feature of SSAS, that would allow us to define a view of a Power BI dataset such that users don’t see other parts of a data model not relevant to their needs. Is the latest Microsoft Power BI update a significant improvement over the previous version? Any specific new features you’d like to highlight? Absolutely. The September update included a Drillthrough feature that, if configured correctly, enables users to quickly access the crucial details associated with values on their reports such as an individual vendor or a product. Additionally, there was a significant update to Report Themes which provides organizations with more control to define standard, consistent report formatting. Drillthrough is so important that an example of this feature was added to the Power BI Cookbook. Additionally, Power BI usage reporting including the identity of the individual user accessing Power BI content was recently released and this too was included in the Power BI Cookbook. Finally, I believe the new Ribbon Chart will be used extensively as a superior alternative to stacked column charts. Can you tell us a little about the new 'time storyteller custom visual' feature in Power BI? The Timeline Storyteller custom visual was developed by the Storytelling with Data group within Microsoft Research. Though it’s available for inclusion in Power BI reports via the Office Store like other custom visuals, it’s more like a storytelling design environment than a single visual given its extensive configuration options for timeline representations, scales, layouts, filtering and annotations. Like the inherent advantages of geospatial visuals, the linking of Visio diagrams with related Power BI datasets can intuitively call out bottlenecks and otherwise difficult-to-detect relationships within processes. 7 reasons to choose Power BI for building enterprise BI solutions Where does Power BI fall within Microsoft's mission to empower every person and every organization on the planet to achieve more of 1. Bringing people together 2. Living smarter 3. Friction free creativity 4. Fluid mobility? Power BI Desktop is available for free and is enhanced each month with features that empower the user to do more and which remove technical obstacles. Similarly, with no knowledge whatsoever of the underlying technology or solution, a business user can access a Power BI app on their phone or PC and easily view and interact with data relevant to their role. Importantly for business analysts and information workers, Power BI acknowledges the scarcity of BI and analytics resources (ie data scientists, BI developers) and thus provides both graphical interfaces as well as full programming capabilities right into Power BI Desktop. This makes it feasible and often painless to quickly create a working, valuable solution with relatively little experience with the product. We can expect Power BI to support 10GB (and then larger) datasets soon as well as improve its ‘data storytelling’ capabilities with a feature called Bookmarks. In effect, Bookmarks will allow Power BI reports to become like PowerPoint presentations with animation. Organizations will also have greater control over how they utilize the v-Cores they purchase as part of Power BI Premium. This will make scaling Power BI deployments easier and more flexible. I’m personally most interested in the incremental refresh feature identified on the Power BI Premium Roadmap. Currently an entire Power BI dataset (in import mode) is refreshed and this is a primary barrier to deploying larger Power BI datasets. Additionally (though not exclusively by any means), the ability to ‘write’ from Power BI to source applications is also a highly anticipated feature on the Power BI Roadmap. How does your book, Microsoft Power BI Cookbook, prepare its readers to be industry ready? What are the key takeaways for readers from this book? Power BI is built with proven, industry leading BI technologies and architectures such as in-memory, columnar compressed data stores and functional query and analytical programming languages. Readers of the Power BI Cookbook will likely be able to quickly deliver fresh solutions or propose ideas for enhancements to existing Power BI projects. Additionally, particularly for BI developers, the skills and techniques demonstrated in the Power BI Cookbook will generally be applicable across the Microsoft BI stack such as in SQL Server Analysis Services Tabular projects and the Power BI Report Server. A primary takeaway from this book is that Power BI is much more than a report authoring or visualization tool. The data transformation and modelling capabilities of Power BI, particularly combined with Power BI Premium capacity and licensing considerations, are robust and scalable. Readers will quickly learn that though certain Power BI features are available in Excel and though Excel can be an important part of Power BI solutions from a BI consumption standpoint, there are massive advantages of Power BI relative to Excel. Therefore, almost all PowerPivot and Power Query for Excel content can and should be migrated to Power BI Desktop. An additional takeaway is the breadth of project types and scenarios that Power BI can support. You can design a corporate BI solution with a Power BI dataset to support hundreds of users across multiple teams but you can also build a tightly focused solution such as monitoring system resources or documenting the contents of a dataset. If you enjoyed this interview, check out Brett’s latest book, Microsoft Power BI Cookbook. Also, read part one of the interview here to see how and where Power BI fits into the BI landscape and what it takes to stay successful in this industry.