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Too often, as designers and developers we fail to make interfaces that are usable, fail to make software that is intuitive, and fail to make products that normal people can understand. By coating design rigour with a layer of brand fluff, and putting form over function again and again, we build products that serve nobody but the internal needs of our corporations and brands. In this interview with Will Grant, a web technology entrepreneur and veteran, we discuss ways to solve 101 UX design problems clearly and single-mindedly. We also discuss about his upcoming book 101 UX Principles, in which Will has defined and refined what it means to build products people intuitively know how to use. Author’s Bio Will Grant is a British UI/UX expert and graduate of Birmingham City University, where he studied human computer interaction and usable design. Following his degree, he trained with Jakob Nielsen and Bruce Tognazzini, pioneers in UX design. Will has been building intuitive usable software products since the birth of the consumer web over 20 years ago, through to the present day, where Will's work has reached more than a billion users. He is the co-founder and the design lead at UX-focused analytics tool Prodlytic. Key takeaways: The vast majority of UX is still about concepts, journey and the tasks we help users to achieve. The tools to deliver great UX have changed, but UX is still about familiarity, consistency and empathy. The 101 UX Principles are a shortcut for UX professionals. Designers can apply them to their products and make usable software 99% of the time for 99% of users. Over reliance on ‘brand’ and internal goals, trying to reinvent the wheel, and forgetting to put oneself in the place of the user are some common reasons why UX design fails. Many UX people forget that design – UI design in particular – isn’t art, it’s design to perform a function: to serve users. Follow Will’s 10 commandments for effective UX design to create more usable and successful products. There’s another 91 in the book 101 UX Principles too. Full Interview Of the 100+ UX design principles that you explore in your new book, if we asked you to pick the top 10, what would those be? Will’s 10 commandments for effective UX design, so to speak. Test with real users Don’t join the dark side Make your buttons look like buttons Label your icon buttons Use 2 font families, maximum Make ‘blank slates’ more than just empty views Hide ‘advanced’ settings from most users Decide if an interaction should be obvious, easy, or possible Anyone can be a UX professional Use device-native input features where possible Just following these 10 and applying them to your software design will create more usable, successful products. There’s another 91 great commandments in the book too. Will, as this book is about 101 UX Principles, what makes your principles right? Nothing is perfect, but these principles are a ‘shortcut’ for UX professionals. Instead of reinventing the wheel, designers can apply these principles to their products and make usable software 99% of the time for 99% of users. I’ve spent over 20 years, since the birth of the consumer web, building interfaces for 100s of products and over a billion users. My approach isn’t perfect, but it has been tested and proven to work at scale. This guide will help you avoid common mistakes and start with a product that’s extremely usable and intuitive - for the widest possible section of users. Why do people keep making UX mistakes? It’s usually a combination of factors; over reliance on ‘brand’ and internal goals, trying to reinvent the wheel, and forgetting to put yourself in the place of the user. Too often the internal goals of an organisation supercede the design teams who are genuinely trying to ‘fight for the user’. The CEO wants it to look a certain way (but he/she has no design background), or the marketing team decide that a certain typeface has to be used (even though it’s unreadable). The paradox is that, as UX and UI people, we’re over-exposed to components, controls, patterns and interfaces in general. It’s the curse of knowledge and we are the last people who should be designing interfaces — unless we can do the hard bit: objectivity. Name a big company that gets UX right, and one that gets it wrong This is impossible, even today after 20+ years of consumer web products, the experience people see is wildly different from product to product - regardless of the company. Generally, large companies with lots of internal bureaucracy and hierarchy produce end products that are the least usable - this is where small, nimble startups can often produce a better product: not because they are ‘better’ overall, but because they haven’t yet lost sight of the importance of UX. And, crucially, startup teams are less encumbered by legacy baggage and are more free to follow best practice in design. Who inspires you the most with the UX community? Donald Norman & Jakob Nielsen have both been hugely influential to me. Don Norman’s book “The design of everyday things”  pretty much kicked off and ‘invented’ the whole field of human-computer interaction, which these days we call ‘UX’. Nielsen & Norman are sometimes derided as ‘too purist’ but that’s what appeals to me most. Stripping back interfaces to the bare minimum, removing clutter and making things simple are things I try to do in my work every day. I worked for a boss in my early 20’s - he wasn’t a designer - but he did fly into an apoplectic rage at the slightest mistake I might make. It taught me to check, check and re-check my designs and despite him being a horrible person, my work is better for it. What was the last app that made you throw down your phone in frustration? Easy - it was the HSBC app, yesterday, with it’s dreadful ‘update’ process. Apple have gone to great lengths to build an App Store which auto-updates your apps, in the background while you’re asleep and your phone is on charge. HSBC decided that their banking app should do its own half-assed updates, whenever it feels like it, inside the app - just when you open the app and you’re about to use it. A classic example of reinventing the wheel, building a new experience that fails because nobody has thought of the user - only of their internal needs. In your more than two decades of UX design experience, how has the web evolved from a user experience perspective? What were some of the biggest surprises in UX design trends for you? What design ideas have remained unaltered by time? I think it’s remarkable how little has changed - in terms of design ideas that ‘just work’ at least. Yes, software has changed massively over that time - from basic websites and browsers on desktop computers through to web app and native apps on smartphones and tablets. However, the vast majority of UX is still about the concepts, the journey and the tasks you’re helping the user to achieve. The tools to deliver great UX have changed, but UX itself is still about familiarity, consistency and empathy. With emerging technologies like machine learning, AR, VR, IoT etc increasingly impacting how we design for the web, where do you see UX design heading in the coming years? What are some general rules worth keeping in mind when designing for the future? What are some opportunities and challenges you foresee for UX designers? It's more of a hope than a prediction, but perhaps us designers will stop doing things because we can and start asking if we should. A greater sense of social responsibility, and a reduction in sneaky 'dark pattern' UX would be great for everyone. Somewhere along the way, many UX people forgot that design – UI design in particular – isn’t art, it’s design to perform a function: to serve users. Too many designers are slavishly following the latest design trend, applying ‘flat design’ to every app, or trying to be different for the sake of it, with custom-designed interfaces and arbitrary visual metaphors. The solution is simple, too: try and be objective, fight for the user, and test with real users as you go. 101 UX Principles provides 101 ways to solve 101 UX problems clearly and single-mindedly. There are 1000s of methods to apply to each and every interaction in your product, but this book is a ‘shortcut’ to a method that works. The book is available to pre-order now and is expected to be published soon. What UX designers can teach Machine Learning Engineers? To start with: Model Interpretability Is your web design responsive? A UX strategy is worthless without a solid usability test plan    

Odoo is one of the fastest growing open source, business application development software products available. It comes with: Powerful GUI, Performance optimization, Integrated in-app purchase features Fast-growing community to transform and modernize businesses We recently interviewed Yenthe Van Ginneken, an Odoo developer, highly active in the Odoo community and recipient of Odoo best contributor of the year 2016 and Odoo community hero 2017. He spoke to us about his journey with Odoo, his thoughts on Odoo’s past, present and future, and the Odoo community. Expert's Bio Yenthe Van Ginneken, currently the technical team leader at Odoo Experts, has been an Odoo developer for over four years. He has won two awards, “Best contributor of the year 2016” and the “Odoo community hero” award in 2017. He loves improving software and teaching other people the best practices for Odoo development on his blog. You can read his Odoo blog, follow him on Twitter or reach out to him on LinkedIn. Key Takeaways Odoo is scalable and flexible to the extent that everyone, from a small startup to a giant tech company can benefit from it. It is ahead of quite a lot of ERP systems with its clean UI, advanced modules integration and the flexibility of its technical framework. Python is the preferred language of choice among most developers that want to use the Odoo framework, especially for automating and scaling tasks. The Odoo community is diverse and vast. By contributing and regularly interacting with other members, you will gain deeper insights into many different aspects of Odoo development. A great way to learn to develop in Odoo and quickly grow is actually by helping in the community. Odoo 12 will reportedly improve data processing, better report insights, and support for OCR (Optical Character Recognition) for handling documents among other exciting updates. Full Interview On who should use Odoo Odoo is more than an ERP tool. According to you, What is Odoo? Who will benefit from adopting Odoo? What made you choose Odoo?   For me, Odoo is more than an ERP. Odoo literally allows me to make any module or functionality that I can think of. Since Odoo is so flexible and scalable I believe that almost everybody can benefit from adopting Odoo. Whether you’re a small start-up or a giant tech company. The most important part to be able to benefit from adopting Odoo is adjusting the processes and mindset to use Odoo, not adjusting Odoo for the company. The projects that work the best and have the best benefit are those that don’t over-engineer and try to focus on the main company processes. I personally chose Odoo after I got an opportunity to become an Odoo developer at a company in Belgium. After the job offer I visited Odoo.com and saw the massive amount of functionalities in Odoo (while being free!) and I was genuinely amazed. After looking at the technical framework and all the default options provided by the framework I was sure that I would love to develop and implement in Odoo. Since that day I never stopped working with Odoo. On journey from OpenERP to Odoo Odoo started off as OpenERP and then in 2014, it moved beyond just ERP and was renamed Odoo. How has Odoo’s journey been so far since then? What do you think are the key milestones achieved by Odoo till date? Since the renaming from OpenERP to Odoo the company has seen a rapid growth. A bit after changing the name Odoo also introduced the enterprise version which was, in my opinion, the turning point for Odoo S.A. It allowed Odoo to keep its open source strength and market share while also gathering funds to fund the ongoing growth of the product. The big investments that are being made in the Research and Development team allow them to keep improving year after year. The main strengths and key milestones from Odoo are absolutely its flexibility, a great framework and the fact that most of the possibilities are already in Odoo by default. On the drive behind contributing to the Odoo community You are highly active in the Odoo community. How did you get into contributing for Odoo? How has this experience improved you as a developer? According to you, what are the key challenges the Odoo community is facing currently? My very first contribution started in the second half of 2014 and weren’t very significant at first. I noticed that Odoo 8, at that point the newest version, was not very well translated and had a lot of inconsistency so I started translating it in Dutch. From there on I noticed that it could have had quite a big impact and in fact could improve the ERP. It didn’t take long before I started contributing in other ways. Reporting issues, fixing bugs, maintaining bug reports and helping other people on the official help forums. By contributing to all these different subjects I got introduced to more domains and gained more insights. Thanks to my involvement with the community, I’ve learned that there is more than one side to developing and implementing projects. I believe it made me a better programmer and made me think a lot more about ways to code custom development for projects. Without being active in a community and contributing you’ll be blindsided by your own perspective. It is a great way to get challenged and you’ll see more cases by being active in the community than you could ever see on your own. The Odoo community faces a few challenges at this point. It is difficult to maintain the right balance between the enterprise version and community (free) version. There are not a lot of very active contributors to the official Odoo code and Odoo is behind on handling fixes/bug reports made by community members. This results in some community members not feeling appreciated or heard. Hiring a second community manager might be a good way to resolve these issues though. The most difficult challenge for both Odoo and the Odoo community is to make everybody feel heard and give every person the ability to contribute in the way he or she can. When there is enough help from Odoo and the community feels supported there is a possibility for a great and thriving community. On how to learn Odoo effectively As a person who has a strong hold over Odoo development, what is the typical learning curve for someone getting into Odoo, as a consultant? What is the best way to start developing in Odoo? What should one watch out for while learning? The learning curve can be quite long and can have its challenges. Usually, if you don’t have any experience with Odoo and only know basic Python it’ll take about six months before you really get to know the ins and outs of Odoo. The best way to learn to develop Odoo is probably the same as with most things in technology: dive in! Make sure you get the basics right and understand how the main functionalities work before going deeper. A great way to learn to develop in Odoo and to quickly grow is actually by helping in the community. You can get insight and help from experienced developers while also contributing to the community, it’s a win-win. Start small and build your way up to the details. It is important to find good documentation and tutorials though. At the moment there are still quite some blog posts and tutorials that are from quite a low quality. Because of this I actually started writing my own tutorials, which explain concepts step by step with samples. You can find it at https://odoo.yenthevg.com Editor’s note: Check out our collection of Odoo Books and Videos to master Odoo development. On the upcoming Odoo 12 release Odoo 12 is expected to be released later this year. What’s got you excited about this new release? Quite a lot! Every release has loads of new features that are announced and it’s an exciting time, every time. The introduction of a report designer for functional people is one of the best (known) new features. The improved reporting tools for data insight will become a great improvement too. The biggest announcements are made at Odoo Experience in October and are not publicly available yet so we’ll have to wait for that. On the future of ERP There is a lot happening in the area of ERP and BI: self-service analytics, real-time analytics, agile BI development etc. Where do you foresee the ERP market headed? We've seen ERP/CRM systems getting powerful inbuilt analytics systems, what do you think is next for the industry? What is Odoo’s role here? As with any sector in IT, a lot is becoming very data-driven. In the future integration and usage of data will only grow. I expect the combination of BI and AI to become a powerful way to process and handle data on unseen scales. Odoo itself has already hinted at improved data processing, better report insights and support for OCR (Optical Character Recognition) for handling documents. Odoo has been ahead of quite a lot of ERP systems with its clean UI, advanced modules integration and the flexibility of its technical framework for years. I expect Odoo will also be leading the way for handling all this data and getting important statistics out of it. I’m quite sure it is only a matter of time before Odoo starts working on even better BI reporting and tools. On Python and automation Automation is everywhere today and becoming an integral part of organizations and processes. Python and automation have gone hand in hand since Python’s early days. Today Python is one of the top programming languages. How do you see Python’s evolution over the years in the area of automation? What are the top ways you use Python for automation, today? It is for a reason that Python is so popular. It is flexible, quite quick to program with and the options are virtually endless. In the next years, Python will only become more popular and this will also be the case for automation projects made with Python. I personally use the Odoo framework with Python as a backbone for nearly everything that I automate (and in fact also for non-automated tasks). The projects vary from automatically handling stock moves to automatically updating remote instances to automatically getting full diagnostic reports. The combination of the programming language and the framework from Odoo allows me to automate tasks and deploy them on a big scale. ERP tool in focus: Odoo 11 How to Scaffold a New module in Odoo 11 A step by step guide to creating Odoo Addon Modules

Despite considerable hype, chaos engineering doesn’t appear to have yet completely captured the imagination of the wider software engineering world. According to this year’s Skill Up survey, when asked, only 13% of developers said they were excited about it. But that doesn’t mean we should disregard - far from it. Like many of the best trends, it might blow up when we least expect. It might find its way onto your CTOs eyes in just a few months. As site reliability engineering grows as a discipline, and as businesses start to put a value on downtime, chaos engineering is likely to become a big part of the reliability and resilience toolkit. Gremlin, chaos engineering, and the end of the age of downtime “People are expected to always be up” says Matt Fornaciari, co-founder and CTO of Gremlin, a product that offers “failure as a service” to businesses. I spoke to Fornaciari last month to get a deeper insight on Gremlin and the team and ideas behind it. He believes the world has changed in recent years, and the days of service windows when sites would just be taken down for an hour or two for an update or change is over: “that’s unacceptable to people these days.” Fornaciari isn’t an unbiased observer, of course. The success of Gremlin depends on chaos engineering’s adoption and acceptance. However, he’s not going out on a limb; there’s clear VC interest in Gremlin. At the end of 2017 the company received their first round of funding - more than 7 million USD. It’s a cliche but money does talk - and in this instance it seems to be saying that this approach might change the way we think about building our software. Arguably, chaos engineering - and by extension Gremlin - is a response to other trends in software. “I’ve seen a lot of signals that this is the way the world’s going”, Fornaciari says. He’s referring here to broader trends like cloud and microservices. He explains that because microservices is all about modularity, and breaking aspects of your software infrastructure into smaller pieces “you end up with nodes in this network” which “adds network complexity.” Consequently, this additional complexity means there is more that can go wrong - it becomes more unreliable. Gremlin’s bid to democratize chaos engineering It’s important to note here that chaos engineering has been around for some time - it’s not a radically new methodology. But it’s largely been locked away in some of the world’s biggest tech companies, like Netflix and Amazon. Many of Gremlin’s leaders actually worked at those companies - Fornaciari has worked at Salesforce and Amazon, for example. “The main goal was to democratize chaos engineering… we’ve [the Gremlin team] done it at the bigger companies and we’re like you know what, everyone can benefit from this”. That is the essential point around chaos engineering. If it’s going to catch on in the mainstream tech world, it needs to be more accessible to different businesses. Fornaciari explains that many of Gremlin’s customers are larger organizations. These are companies for whom downtime is of utmost importance, where a site outage that lasts just an hour could cost thousands of dollars. That said, from a cultural perspective, many organizations find it difficult to adopt this sort of mindset. “Proving the value of something that doesn’t happen,” Fornaciari says, is one of the biggest challenges for Gremlin. This is particularly true when selling their tool. Pager pain: How Gremlin sells chaos engineering to customers This is how Gremlin does it: “We have three qualifying questions: do you measure your downtime? Do you have somebody who’s responsible for downtime? And do you actually have a dollar amount tied to it?” Presumably, for many organizations at least one answer to these questions is “no”. That’s why customer support is so important for Gremlin. “Customer success and developer advocacy are two of our biggest initiatives… I’ve told people as we’re recruiting them that half of our goal as a company is to educate people.” Gremlin’s challenges as a product and as a business reflect the wider difficulties of managing upwards. The tension between those ‘on the ground’ and those at a more senior and managerial level is one that Gremlin is acutely aware of. This is where a lot of push back comes from, Fornaciari explains: What we’ve seen so far is just push back from top down - like, why do we need this? We use the term pager pain to define the engineer on call - the closer you are to the ground the closer you are to the on call rotation and the more you feel those pains and the more you believe in this but as you raise up a couple of levels you maybe don’t feel that as much… if you don’t have that measure on uptime - unless someone is on the hook for that at a higher level there’s oftentimes a why do we need this, why are we going to spend money on breaking things. Pager pain is a nice concept - it captures the tension between different layers of management. It highlights the conflict between ‘what do we need?’ and ‘what can we do?’ Read next: Blockchain can solve tech's trust issues  Safety, simplicity and security To successfully sell Gremlin, the way the product is designed is everything. For that reason, the Gremlin team have three tenets built into their product: safety, security, simplicity. When you’ve got a “potentially dangerous tool,” as Fornaciari himself describes it, making sure things are safe and secure is absolutely essential. Arguably, the fact that chaos engineering is so hard to do well might be something that Gremlin can use to its advantage. “One thing we hear when we talk to companies about it is ‘well we’ll go build this ourselves’ and the fact is it’s a really hard thing to do, and a hard thing to do well.” Gremlin is walking on a bit of a tightrope. On the one hand chaos engineering is for everyone, but on the other it’s difficult and dangerous. It should be accessible, but not too accessible. “One of the reasons we don’t have a free offering is because we are a little worried about protecting our customers not doing any harm to people… I mean, this is essentially giving somebody a potentially dangerous tool.. If they’re not given the proper education then that could be a problem, right?” Gremlin aren’t the only chaos engineering product out there. As with any trend, there are plenty of software platforms and tools emerging for technologically forward thinking businesses. Fornaciari doesn’t see these as a threat - he’s confident, bullish even, about Gremlin’s place in the market. “There are a lot of tools out there that people can go and use but they really lack the safety and simplicity.” Alongside its philosophy of safety, security and simplicity, a big selling point, according to Fornaciari, is the experience and expertise that is built into Gremlin’s DNA. “We’ve got fifteen years of combined expertise in this space” he says. “Being the experts on it and having built it 3 or 4 times already in different big companies, it sort of gave us this leg up to go out there in the world.” But while Fornaciari is eager to assert Gremlin’s knowledge, there’s no trace of elitism - sharing knowledge is a core part of the product offering. “We actually built out customer success tooling so we can see if particular attacks fail for them we can actually proactively reach out and be like ‘hey we saw you were trying to do this, maybe you meant to do this’” Fornaciari explains. Controlled chaos: chaos engineering and the scientific method Control is central to Gremlin’s philosophy - it’s a combination of the team’s commitment to safety, security and simplicity. In fact, this element of control that distinguishes chaos engineering today, from what went before. Central to Gremlin’s mission to make chaos engineering accessible, is also redefining how it’s done. “If you’re familiar with the netflix chaos monkey mentality of randomly terminating services, well that’s a good start, but safety is really lacking. We talked more about this controlled chaos… this idea that you start fairly small with this small blast radius and then as you become more confident you grow it out and grow it out as opposed to just like ‘cool, let’s just chuck a grenade in here and see what happens.’” Fornaciari goes on to describe this ‘controlled chaos’ in a surprising way. “It’s much more like the scientific method actually. Applying that method to your infrastructure and your reliability in general.” This approach is essential if you’re going to do chaos engineering well. How to do chaos engineering effectively When I ask Fornaciari how engineering teams and businesses can do chaos engineering well he emphasizes the importance of starting with a hypothesis: “You need to have a hypothesis that you’re trying to prove.Throwing random chaos at something is fine - it’ll sort of surface some of the unknown unknowns for you. But really having a hypothesis that you’re trying to prove is the best way to get value out of this [chaos engineering].” If you’re going to take a scientific approach to testing your infrastructure using ‘chaos experiments’, managing scale is also incredibly important. Don’t run before you can walk is the message. “Keep it very small initially, then you start to grow the blast radius. You definitely want to make sure that you’re starting off with the smallest modicum that you can.” Given the potential dangers of throwing metaphorical gremlins into your system, starting where your comfortable makes a lot of sense. “Start in staging, start where your comfortable, build your confidence. Make sure your system behaves well in front of non-customer facing traffic before you go out to the world.” That said, Gremlin have had “some pretty bold customers” who go straight ahead and start running chaos experiments in production. “That was cool. It’s a little scary, but they were confident and they’ve been using Gremlin as part of their system ever since.” Chaos engineering requires confidence and control Ultimately, if chaos engineering is going to take off - as Fornaciari believes it will - engineers will need to be incredibly confident. That’s true on a number of levels. You need confidence that you’ll be able to handle a range of experiments and deploy them wisely. But you’ll also need confidence that you can manage the expectations of those in senior management. It’s not hard to see the value of chaos engineering. As Fornaciari says “if you prevent one outage one time, you’ve saved that money to pay for the tool to make sure it doesn’t happen again.” But it might be hard to find time for it. It might be hard to get buy in and investment in the tools you need to do it. Gremlin are certainly going to play an important part in helping engineers do that. But one of its biggest challenges - and perhaps one of its most noble missions too - is transforming a culture where people don’t really appreciate ‘pager pain’. If Fornaciari and Gremlin can help solve that, good luck to them. You can follow Matt Fornaciari on Twitter: @callmeforni

The hype around blockchain has now reached fever pitch. Now the Bitcoin bubble has all but burst, it would seem that the tech world - and beyond - is starting to think more creatively about how blockchain can be applied. We've started to see blockchain being applied in a huge range of areas; that's likely to grow over the next year or so. We certainly weren't surprised to see blockchain rated highly by many developers working in a variety of fields in this year's Skill Up survey. Around 70% of all respondents believe that blockchain is going to prove to be revolutionary. Read the Skill Up report in full. Sign up to our weekly newsletter and download the PDF for free. To help us make sense of the global enthusiasm and hype for blockchain, we spoke to blockchain expert Imran Bashir. Imran is the author of Mastering blockchain, so we thought he could offer some useful insights into where blockchain is going next. He didn't disappoint. Respondents to the Skill Up survey said that blockchain would be revolutionary. Do you agree? Why? I agree. The fundamental issue that blockchain solves is that of trust. It enables two or more mutually distrusting parties to transact with each other without the need of establishing trust and a trusted third party. This phenomenon alone is enough to start a revolution. Generally, we perform transactions in a centralised and trusted environment, which is a norm and works reasonably well but think about a system where you do not need trust or a central trusted third party to do business. This paradigm fundamentally changes the way we conduct business and results in significant improvements such as cost saving, security and transparency. Why should developers learn blockchain? Do you think blockchain technology is something the average developer should be learning? Why? Any developer should learn blockchain technology because in the next year or so there will be a high demand for skilled blockchain developers/engineers. Even now there are many unfilled jobs, it is said that there are 14 jobs open for every blockchain developer. The future will be built on blockchain; every developer/technologist should strive to learn it. What most excites you about blockchain technology? It is the concept of decentralisation and its application in almost every industry ranging from finance and government to medical and law. We will see applications of this technology everywhere. It will change our lives; just the way Internet did in the 1990s. Also, smart contracts constitute a significant part of blockchain technology, and it allows you to implement Contracts that are automatically executable an enforceable. This ability of blockchain allows you drastically reduce the amount of time it takes for contract enforcement and eliminates the need for third parties and manual processes that can take a long time to come into action. Enforcement in the real world takes a long time, in blockchain world, it is reduced to few minutes, if not seconds, depending on the application and requirements. What tools do you need to learn to take advantage of blockchain? What tools do you think are essential to master in order to take advantage of blockchain? Currently, I think there are some options available. blockchain platforms such as Ethereum and Hyperledger fabric are the most commonly used for development. As such, developers should focus on at least one of these platforms. It is best to start with necessary tools and features available in a blockchain, and once you have mastered the concepts, you can move to using frameworks and APIs, which will ease the development and deployment of decentralised applications. What do you think will be the most important thing for developers to learn in the next 12 months? Learn blockchain technology and at least one related platform. Also explore how to implement business solutions using blockchain which results in bringing about benefits of blockchain such as security, cost-saving and transparency. Thanks for taking the time to talk to us Imran! You can find Imran's book on the Packt store.

This year's Skill Up survey produced some interesting results when it came to the best front end frameworks. Angular remains the most established tool with 40% of web developers reporting that they used it regularly. React is actually a little further behind, with 25% using it regularly. Similarly, Vue.js is growing but used by 20% of respondents. However, opinion was a little different when we asked what front end frameworks should win the battle of the 3 big front end tools. Respondents were split on Angular and React, with both JavaScript tools winning 34% of the vote. Vue wasn't far behind, at just over 30%. With the web development world apparently split over what framework is going to define the future of the field, how are we to pick them apart? Or do we even really need to worry? Read the report in full. Sign up to our weekly newsletter and download the PDF for free. The fact that we have three great front end tools jostling for developer attention is surely a good thing, right? To help us make sense of these trends, we caught up with Angular expert Minko Gechev to find out what he makes of web development in 2018, and what front end developers should be learning. Minko Gechev is the author of Switching Angular. You can find the latest edition here on the Packt Store. Which front end framework should you learn: Angular, React, or Vue? Respondents to the Skill Up survey were evenly split between Angular, React, and Vue in the 'battle of the frameworks'. Which do you think developers should learn, and why? In all of them, there are unique and interesting ideas which are worth exploring. I truly believe that learning all the major frameworks can help developers go to the next level! This doesn’t necessary mean to be proficient in all of them. Having a high-level understanding of how the frameworks work and how to use them is completely enough and will allow you to adapt according to the projects’ requirements. This is similar to learning programming languages from different paradigms – it helps you discover how problems are being solved in different ways. For the past a couple of years, the redux pattern became the de facto standard for state management in the modern front-end development. The good thing about redux is that it’s view agnostic so you can use it with any framework – Vue, React, Angular, etc. Angular has its own redux alternative called ngrx which empowers a declarative approach with RxJS but in general, it follows the same underlying pattern. My recommendation would be to understand how to manage the state of our applications because that’s probably the most complex problem that we’re solving in our day to day development process. Once we have a solid understanding of this, we can easily switch between different frameworks depending on the problems we’re solving, what the rest of the team is using, and the project’s requirements. A very interesting characteristic of learning Angular is that if we get comfortable with the framework we’d be also familiar with TypeScript, RxJS, and techniques such as dependency injection. This may look like an initial overhead but it’s a great long-term investment which pays off really well in large projects. How important is TypeScript to front end development? How important is TypeScript to modern web development? Why? Over the past a couple of years I see a strong increase in the excitement around the language, not only in the Angular world but also in React and Vue. I’m personally using TypeScript for a few projects – a platform that we built with React and an educational application written in Angular. I see a lot of value in using TypeScript. Recently I haven’t started any project with JavaScript – for everything new I’m using TypeScript and I’m trying to migrate, as many of my existing projects as possible.There are a few reasons for this: TypeScript provides great development experience! Especially, combined with VSCode, you can instantly notice when you’ve misspelled a property, method, you’re trying to access a property of a nullable value, etc. It gives you a sense of security that your program is correct to given extent. Of course, TypeScript cannot save us from logical mistakes but if we use its type system wisely, we can get great benefits. You might be curious what benefits? Well, TypeScript can help us reduce the number of bugs in our programs. In the study “To Type or Not to Type: Quantifying Detectable Bugs in JavaScript” the authors shown that the average JavaScript program can benefit with 15% bug reduction if it uses the type system of TypeScript. The study was using TypeScript version 2.0; with the latest features of the language the number of detectable bugs is growing dramatically. Since recently webpack is leveraging TypeScript as well because it helps discover already existing issues in the codebase. Web developers and JavaScript fatigue Do you think we're past web developers experiencing 'JavaScript fatigue'? JavaScript is very dynamic and it moves very quickly. There are a lot of potential issues which could be caused by a variety of reasons. With semantic versioning and powerful type systems (such as the type system of TypeScript), we’re walking in the right direction but we definitely have a long way to go until the ecosystem matures. Web development over the next 12 months: WebAssembly and machine learning What do you think will be the most important thing for developers to learn in the next 12 months? There are a lot of exciting things happening nowadays! Web browsers are getting more and more powerful, exposing hundreds of APIs and opportunities. WebAssembly is moving very quickly and I believe that together with Rust it has a lot of potential in future. On the other hand, Google recently announced TensorFlow.js. This is a library which allows us to use machine learning (ML) in the browser. In the next years, ML is going to take a larger portion of our development process (directly or indirectly) for: Implementing features in our applications Improving development process I’m specifically interested in the second point – improving our development process by using ML. Together with Addy Osmani, Kyle Mathews, and Katie Hempenius, we’ve been working on a toolkit called Guess.js. It aims to provide predictive bundling and pre-fetching based on ML techniques in order to let us develop faster Angular/React/Vue/etc. applications. I’m really excited about what’s coming up in near future! So are we! Thanks for taking the time to speak to us, Minko!

In the past few years, Agile software development has seen tremendous growth. There is a huge demand for software delivery solutions that are fast, yet flexible to numerous amendments. As a result, Continuous Integration (CI) and Continuous Delivery (CD) methodologies are gaining popularity. They are considered to be the cornerstones of DevOps and drive the possibilities of modern architectures like microservices and cloud native. Author’s Bio Nikhil Pathania, a DevOps practitioner at Siemens Gamesa Renewable Energy, started his career as an SCM engineer and later moved on to learn various tools and technologies in the fields of automation and DevOps. Throughout his career, Nikhil has promoted and implemented Continuous Integration and Continuous Delivery solutions across diverse IT projects. He is the author of Learning Continuous Integration with Jenkins. In this exclusive interview, Nikhil gives us a sneak peek into the trends and challenges of Continuous Integration in DevOps. Key Takeaways The main function of Continuous Integration is to provide feedback on integration issues. When practicing DevOps, a continuous learning attitude, sharp debugging skills, and an urge to improvise processes is needed Pipeline as a code is a way of describing a Continuous Integration pipeline in a pre-defined syntax One of the main reasons for Jenkin’s popularity is it’s growing support via plugins Making yourself familiar with a scripting language like Shell or Python will help you accomplish difficult tasks related to CI/CD Continuous Integration is built on Agile and requires a fair understanding of the 12 principles. Full Interview On the popularity of DevOps DevOps as a concept and culture is gaining a lot of traction these days. What is the reason for this rise in popularity? What role does Continuous Integration have to play in DevOps? To understand this, we need to look back at the history of software development. For a long period, the Waterfall model was the predominant software development methodology in practice. Later, when there was a sudden surge in the usage and development of software applications, the Waterfall model proved to be inefficient, thus giving rise to the Agile model. This new model proposed coding, building, testing, packaging, and releasing software in a quick and incremental fashion. As the Agile model gained momentum, more and more teams wanted to ship their applications faster and more frequently. This added a huge pressure on the release management process. To cope up with this pressure, engineers came up with new processes and techniques (collectively bundled as DevOps), such as the usage of improved branching strategies, Continuous Integration, Continuous Delivery, Automated environment provisioning, monitoring, and configuration. Continuous Integration involves continuous building and testing of your integrated code; it’s an integral part of DevOps, dealing with automated builds, testing, and more. Its core function is to provide a quick feedback on the integration issues. On your journey as a DevOps engineer You have been associated with DevOps for quite some time now and hold vast experience as a DevOps engineer and consultant. How and when did your journey start? Which tools did you master to help you with your day-to-day tasks? I started my career as a Software Configuration Engineer and was trained in SCM and IBM Rational Clearcase. After working as a Build and Release Engineer for a while, I turned towards new VCS tools such as Git, automation, and scripting. This is when I was introduced to Jenkins followed by a large number of other DevOps tools such as SonarQube, Artifactory, Chef, Teamcity, and more. It’s hard to spell out the list of tools that you are required to master since the list keeps increasing as the days pass by. There is always a new tool in the DevOps tool chain replacing the old one. A DevOps tool itself changes a lot in its usage and working over a period of time. A continuous learning attitude, sharp debugging skills, and an urge to improvise processes is what is needed, I’ll say. On the challenges of implementing Continuous Integration What are some of the common challenges faced by engineers in implementing Continuous Integration? Building the right mind-set in your organization: By this I mean preparing teams in your organisation to get Agile. Surprised! 50% of the time we spend at work is on migrating teams from old ways of working to the new ones. Implementing CI is one thing, while making the team, the project, the development process, and the release process ready for CI is another. Choosing the right VCS tool and CI tool: This is an important factor that will decide where your team will stand a few years down the line—rejoicing in the benefits of CI or shedding tears in distress. On how the book helps overcome these challenges How does your book 'Learning Continuous Integration with Jenkins' help DevOps professionals overcome the aforementioned challenges? This is why I have a whole chapter (Concepts of Continuous Integration) explaining how Continuous Integration came into existence and why projects need it. It also talks a little bit about the software development methodologies that gave rise to it. The whole book is based on implementing CI using Jenkins, Git, Artifactory, SonarQube, and more. About Pipeline as a Code Pipeline as a Code was a great introduction in Jenkins 2. How does it simplify Continuous Integration? Pipeline as a code is a way of describing your Continuous Integration pipeline in a pre-defined syntax. Since it’s in the form of code, it can be version-controlled along with your source code and there are endless possibilities of programming it, which is something you cannot get with GUI pipelines. On the future of Jenkins and competition Of late, tools such as TravisCI and CircleCI have got a lot of positive recognition. Do you foresee them going toe to toe with Jenkins in the near future? Over the past few years Jenkins has grown into a versatile CI/CD tool. What makes Jenkins interesting is its huge library of plugins that keeps growing. Whenever there is a new tool or technology in the software arena, you have a respective plugin in Jenkins for it. Jenkins is an open source tool backed by a large community of developers, which makes it ever-evolving. On the other hand, tools like TravisCI and CircleCI are cloud-based tools that are easy to start with, limited to CI in their functionality, and work with GitHub projects. They are gaining popularity mostly in teams and projects that are new. While it’s difficult to predict the future, what I can say for sure is that Jenkins will adapt to the ever-changing needs and demands of the software community. On key takeaways from the book Learning Continuous Integration with Jenkins Coming back to your book, what are the 3 key takeaways from it that readers will find to be particularly useful? In-depth coverage of the concepts of Continuous Integration. A step-by-step guide to implementing Continuous Integration, Continuous Delivery with Jenkins 2 using all the new features. A practical usage guide to Jenkins's future, the Blue Ocean. On the learning path for readers Finally, what learning path would you recommend for someone who wants to start practicing DevOps and, specifically, Continuous Integration? What are the tools one must learn? Are there any specific certifications to take in order to form a solid resume? To begin with, I would recommend learning a VCS tool (say Git), a CI/CD tool (Jenkins), a configuration management tool (Chef or Puppet, for example), a static code analysis tool, a cloud tool like AWS or Digital Ocean, and an artifactory management tool (say Artifactory). Learn Docker. Build a solid foundation in the Build, Release and Deployment processes. Learn lots of scripting languages (Python, Ruby, Groovy, Perl, PowerShell, and Shell to name a few), because the real nasty tasks are always accomplished by scripts. A good knowhow of the software development process and methodologies (Agile) is always nice to have. Linux and Windows administration will always come in handy. And above all, a continuous learning attitude, an urge to improvise the processes, and sharp debugging skills is what is needed. If you enjoyed reading this interview, check out Nikhil’s latest edition Learning Continuous Integration with Jenkins. Top 7 DevOps Tools in 2018 Everything you need to know about Jenkins X 5 things to remember when implementing DevOps

Tableau is one of the leading BI tools used by data science and business intelligence professionals today. You can not only use it to create powerful data visualizations but also use it to extract actionable insights for quality decision making thanks to the plethora of tools and features it offers. We recently interviewed Joshua Milligan, a Tableau Zen Master and the author of the book, Learning Tableau. Joshua takes us on an insightful journey into Tableau explaining why it is the Google of data visualization. He tells us all about its current and future focus areas such as Geospatial analysis and automating workflows, the exciting new features and tools such as Hyper, Tableau Prep among other topics.  He also gives us a preview of things to come in his upcoming book. Author’s Bio Joshua Milligan, author of the bestselling book, Learning Tableau, has been with Teknion Data Solutions since 2004 and currently serves as a principal consultant.  With a strong background in software development and custom .NET solutions, he brings a blend of analytical and creative thinking to BI solutions. Joshua has been named Tableau Zen Master, the highest recognition of excellence from Tableau Software not once but thrice. In 2017, Joshua competed as one of three finalists in the prestigious Tableau Iron Viz competition. As a Tableau trainer, mentor, and leader in the online Tableau community, he is passionate about helping others gain insights from their data. His work has been featured multiple times on Tableau Public’s Viz of the Day and Tableau’s website. He also shares frequent Tableau (and Maestro) tips, tricks, and advice on his blog VizPainter.com. Key Takeaways Tableau is perfectly tailored for business intelligence professionals given its extensive list of offerings from data exploration to powerful data storytelling. The drag-and-drop interface allows you to understand data visually thus enabling anyone to perform and share self service data analytics with colleagues in seconds. Hyper is new in-memory data engine designed for powerful query analytical processing on complex datasets. Tableau Prep, a new data preparation tool released with Tableau 2018.1, allows users to easily combine, shape, analyze and clean the data for compelling analytics. Tableau 2018.1 is expected to bring new geospatial tools, enterprise enhancements to Tableau Server, and new extensions and plugins to create interactive dashboards. Tableau users can expect to see artificial intelligence and machine learning becoming major features in both Tableau and Tableau Prep - thus deriving insights based on users behavior across the enterprise. Full Interview There are many enterprise software for business intelligence, how does Tableau compare against the others? What are the main reasons for Tableau's popularity? Tableau's paradigm is what sets it apart from others. It's not just about creating a chart or dashboard. It's about truly having a conversation with the data: asking questions and seeing instant results as you drag and drop to get new answers that raise deeper questions and then iterating. Tableau allows for a flow of thought through the entire cycle of analytics from data exploration through analysis to data storytelling.  Once you understand this paradigm, you will flow with Tableau and do amazing things! There's a buzz in the developer's community that Tableau is the Google of data visualization. Can you list the top 3-5 features in Tableau 10.5 that are most appreciated by the community? How do you use Tableau in your day-to-day work? Tableau 10.5 introduced Hyper - a next-generation data engine that really lays a foundation for enterprise scaling as well as a host of exciting new features and Tableau 2018.1 builds on this foundation.  One of the most exciting new features is a completely new data preparation tool - Tableau Prep. Tableau Prep complements Tableau Desktop and allows users to very easily clean, shape, and integrate their data from multiple sources.  It’s intuitive and gives you a hands-on, instant feedback paradigm for data preparation in a similar way to what Tableau Desktop enables with data visualization. Tableau 2018.1 also includes new geospatial features that make all kinds of analytics possible.  I’m particularly excited about support for the geospatial data types and functions in SQL Server which have allowed me to dynamically draw distances and curves on maps.  Additionally, web authoring in Tableau Server is now at parity with Tableau Desktop. I use Tableau every day to help my clients see and understand their data and to make key decisions that drive new business, avoid risk, and find hidden opportunities.  Tableau Prep makes it easier to access the data I need and shape it according to the analysis I’ll be doing. Tableau offers a wide range of products to suit their users' needs. How does one choose the right product from their data analytics or visualization need? For example, what are the key differences between Tableau Desktop, Server and Public? Are there any plans for a unified product for the Tableau newbie in the near future? As a consultant at Teknion Data Solutions (a Tableau Gold Partner), I work with clients all the time to help them make the best decisions around which Tableau offering best meets their needs.  Tableau Desktop is the go-to authoring tool for designing visualizations and dashboards. Tableau Server, which can be hosted on premises or in the cloud, gives enterprises and organizations the ability to share and scale Tableau.  It is now at near parity with Tableau Desktop in terms of authoring. Tableau Online is the cloud-based, Tableau managed solution. Tableau Public allows for sharing public visualizations and dashboards with a world-wide audience. How good is Tableau for Self-Service Analytics / automating workflows? What are the key challenges and limitations? Tableau is amazing for this. Combined with the new data prep tool - Tableau Prep - Tableau really does offer users, across the spectrum (from business users to data scientists), the ability to quickly and easily perform self-service analytics. As with any tool, there are definitely cases which require some expertise to reach a solution. Pulling data from an API or web-based source or even sometimes structuring the data in just the right way for the desired analysis are examples that might require some know-how. But even there, Tableau has the tools that make it possible (for example, the web data connector) and partners (like Teknion Data Solutions) to help put it all together. In the third edition of Learning Tableau, I expand the scope of the book to show the full cycle of analytics from data prep and exploration to analysis and data storytelling. Expect updates on new features and concepts (such as the changes Hyper brings), a new chapter focused on Tableau Prep and strategies for shaping data to perform analytics, and new examples throughout that span multiple industries and common analytics questions. What is the development roadmap for Tableau 2018.1? Are we expecting major feature releases this year to overcome some of the common pain areas in business intelligence? I'm particularly excited about Tableau 2018.1. Tableau hasn't revealed everything yet, but things such as new geospatial tools and features, enterprise enhancements to Tableau Server, the new extensions API, new dashboard tools, and even a new visualization type or two look to be amazing! Tableau is working a lot in the geospatial domain coming up with new plugins/connectors and features. Can we expect Tableau to further strengthen their support for spatial data? What are the other areas/domains that Tableau is currently focused on? I couldn't say what the top 3-5 areas are - but you are absolutely correct that Tableau is really putting some emphasis on geospatial analytics.  I think the speed and power of the Hyper data engine makes a lot of things like this possible. Although I don't have any specific knowledge beyond what Tableau has publicly shared, I wouldn't be surprised to see some new predictive and statistical models and expansion of data preparation abilities. What's driving Tableau to Cloud? Can we expect more organizations adopting Tableau on Cloud? There has been a major shift to the cloud by organizations. The ability to manage, scale, ensure up-time, and save costs are driving this move and that in turn makes Tableau's cloud-based offerings very attractive. What does Tableau's future hold, according to you? For example, do you see machine learning and AI-powered analytics platform transformation? Or can we expect Tableau entering the IoT and IIoT domain? Tableau demonstrated a concept around NLQ at the Tableau Conference and has already started building in a few machine learning features. For example, Tableau now recommends joins based on what is  learns from behavior of users across the enterprise. Tableau Prep has been designed from the ground-up with machine learning in mind. I fully expect to see AI and machine learning become major features in both Tableau and Tableau Prep – but true to Tableau’s paradigm, they will complement the work of the analyst and allow for deeper insight without obscuring the role that humans play in reaching that insight.  I'm excited to see what is announced next! Give us a sneak peek into the book you are currently writing "Learning Tableau 2018.1, Third Edition", expected to be released in the 3rd Quarter this year. What should our readers get most excited about as they wait for this book? Although the foundational concepts behind learning Tableau remain the same, I'm excited about the new features that have been released or will be as I write.  Among these are a couple of game-changers such as the new geospatial features and the new data prep tool: Tableau Prep. In addition to updating the existing material, I'll definitely have a new chapter or two covering those topics! If you found this interview to be interesting, make sure you check out other insightful articles on business intelligence: Top 5 free Business Intelligence tools [Opinion] Tableau 2018.1 brings new features to help organizations easily scale analytics [News] Ride the third wave of BI with Microsoft Power BI [Interview - Part 1] Unlocking the secrets of Microsoft Power BI [Interview - Part 2] How Qlik Sense is driving self-service Business Intelligence [Interview]

Python is currently one of the most popular and desired programming languages. Primarily for its simplicity, agility and ability to be used across a variety of software development projects. Although an Object Oriented language, Python supports an array of programming paradigms, including Functional Programming. Functional Programming is a paradigm that treats computation as the evaluation of math functions. It’s quite advantageous as it allows for efficient parallel programming and error-free code. We recently interviewed Steven Lott, a true Python professional and best-selling author of a number of Python books. Steven talks a bit about modern Python and how the language adapts well to the Functional paradigm, offering developers a range of solutions to build modern, cloud based applications. He also talks about his most recent book, the second edition of his best seller, Functional Python Programming, and how it will benefit developers looking to enter the world of functional programming with Python. About Steven Steven F. Lott has been programming since the 70s, when computers were large, expensive, and rare. As a contract software developer and architect, he has worked on hundreds of projects, from very small to very large. He's been using Python to solve business problems for over 10 years. Steven is a technomad who lives in various places on the east coast of the U.S. Follow his technology blog to stay updated on the latest trends in tech. You may also connect with him on LinkedIn. Key Takeaways Why learn Python? One of the major reasons developers appreciate Python, is because it’s simple, and has the ability to create succinct and expressive programs. For example, data scientists prefer Python because they can build sophisticated analytical tools using simple functions and produce useful results. Why Functional programming? The functional programming paradigm forms the perfect foundation for developers and architects to build and design modern architectures like Serverless. But Python isn’t inherently functional. Although an object oriented language at heart, Python can create higher order functions and other functional features. Python 3 has made this easier. Steven’s Python 4 wish list: In future versions of Python, Steven hopes to see a wider use of Unicode operator characters like × in addition, * for multiplication, and ÷ in addition to / for division. Also, he expects PyPy and RPython projects to be more widely used; future Pythons versions will benefit from optimizations and restructuring the interpreter. One of the most helpful features of Python 3 are type hints, which allow one to write clear and implicitly documented code while preventing the invoking of methods with wrong data types. Steven’s latest edition of Functional programming with Python, explores type hints in depth along with core language features such as lambdas, generator expressions, functions, and callable objects. Full Interview Python is one of the top programming languages. List down top 3 features of Python that make programmers love it.. It seems like programmers love Python primarily because it allows them to create succinct, expressive programs. Before long, they learn the vast library of code - is another reason for Python's immense popularity. Many people adopt Python because of the low barrier to entry: it really is as simple as download and start working. You've been working with Python for over a decade. How has your experience been with Python as a primary development language? Over my 40-year career, I've used a variety of languages. And I've found Python to be extremely productive. A team can build and deploy microservices-based applications at a tremendous pace. Data scientists can build sophisticated analytical tools using simple functions to produce useful results without the overheads of complex compile and build environments. Back when Python 3 just came into existence, we saw certain resistance to the notion of Python being apt for functional programming. How would you say Python has progressed since then? At its core, Python is an object-oriented language. Consequently some functional features aren't central. One of the essential functional design tools -- creating higher-order functions -- has always been part of Python. The wider use of generator functions in Python 3 has made functional Python programming much more common. Many Python applications are hybrids, mixing object-oriented and functional features of the language. Now that type hints are available, it becomes practical to use mypy to confirm that the code is very likely to work properly. For a developer who's picking up the Functional Programming paradigm for the first time, what do you think are the prerequisites? Functional programming is closely aligned to the core mathematical ideas of functions and functional composition. As a consequence, a minimal background in programming could be advantageous to help leverage essential function definitions and avoid needless state change. For programmers already heavily invested in procedural programming, it may be helpful to set the idea of stateful objects aside. In the above context, how does your book, Functional Python Programming, Second Edition, prepare its readers to be industry ready? What are the key takeaways for readers from your title and how does it help with the learning curve? The examples in the book are related to exploratory data analysis, an important skill in the broader area of data science. They also focus on the standard library, allowing someone to apply the functional design approach to other libraries and tools. I think a focus on the core language features (e.g., lambdas, generator expressions, functions, callable objects) provide a foundation that allows a programmer to apply the core ideas more widely to different kinds of problems and other software packages. What new and updated content is available in this edition, for developers who've purchased your previous book? Almost all of the examples have been rewritten to include type hints. This can be an important quality check helping to ensure the Python code works. When used with doctest examples, it becomes relatively easy to provide reliable, correct code. In a few cases, external packages (i.e., the pymonad library) don't have type hints and the examples reflect this gap. Can you throw some light on Functional Reactive Programming and how FRP with Python is boosting the implementation of modern architectures like Cloud Native and Serverless? The central idea of serverless programming -- a collection of isolated functions -- fits the functional programming paradigm very elegantly. The processing is generally stateless, with stand-alone functions waiting for their inputs. Ideas like "choreography" of web services work with this idea of stateless functions that respond to an input by producing an output. This leads to careful separation of persistence and state change from the other transformational processing. This helps create software with easy-to-understand behavior and implementation code that's very expressive of the algorithm. As Python inches towards a 4.0, what do you think should/can be changed/rectified in the language, for the better? At some point, I expect the PyPy and RPython projects to create some optimizations leading to a fundamental restructuring of the interpreter. Perhaps these changes could remove the need for the GIL (Global Interpreter Lock) by exposing a minimal kernel of code that requires exclusive access to internal data structures. Of more general interest, I'd hope to see wider use of Unicode operator characters like × in addition to * for multiplication, and ÷ in addition to / for division. Perhaps ∻ could be adopted for truncated division. This could also lead to use of ℝ instead of float and ℤ instead of int providing a more mathematical look to type hints. It would be nice to expand the use of the Unicode character set to create more readable programs. List down 3 reasons for developers to choose your book as the book of choice for Functional programming in Python. Programmers who want to create succinct and expressive code often find functional design to help them fulfill this. The Functional Python Programming book provides extensive examples that show multiple ways to achieve this goal. In many cases, generator functions and lazy processing can be a large performance improvement. A generator function can use less memory than a large data collection, and this change can be helpful. This book will provide number of examples of lazy processing to avoid creating large, in-memory collections. It can be difficult to get started with type hints. The use case in this book show type hints in somewhat more complex real-world situations. If you enjoyed this interview, head over to check out Steven’s latest edition of Functional Python Programming, He is leveraging Python to implement microservices and ETL pipelines. His other titles with Packt Publishing include Python Essentials, Mastering Object-Oriented Python, Functional Python Programming, and Python for Secret Agents. What is the difference between functional and object oriented programming? Building functional programs with F# Seven wrongs don’t make the one right: Solving a problem with Functional Javascript

It comes as no surprise to many developers, Python has grown to become the preferred language of choice for data science. One of the reasons for its staggering adoption in the data science community is the rich suite of libraries for effective data analysis and visualization - allowing you to extract useful, actionable insights from your data. Pandas is one such Python-based library, that provides a solid platform to carry out high-performance data analysis. Ted Petrou is a data scientist and the founder of Dunder Data, a professional educational company focusing on exploratory data analysis. Before founding Dunder Data, Ted was a data scientist at Schlumberger, a large oil services company, where he spent the vast majority of his time exploring data. Ted received his Master’s degree in statistics from Rice University and has used his analytical skills to play poker professionally. He taught math before becoming a data scientist. He is a strong supporter of learning through practice and can often be found answering questions about pandas on Stack Overflow. In this exciting interview, Ted takes us through an insightful journey into pandas - Python’s premier library for exploratory data analysis, and tells us why it is the go-to library for many data scientists to discover new insights from their data. Key Takeaways Data scientists are in the business of making predictions. To make the right predictions you must know how to analyse your data. to perform data analysis efficiently, you must have a good understanding of the concepts as well be proficient using the tools like pandas. Pandas Cookbook contains step by step solutions to the master the pandas syntax while going through the data exploration journey (missteps et al) to solve the most common and not-so-common problems in data analysis. Unlike R which has several different packages for different data science tasks, pandas offers all data analysis capabilities as a single large Python library. Pandas has good time-series capabilities, making it well-suited for building financial applications. That said, its best use is in data exploration - to find interesting discoveries within the data. Ted says beginners in data science should focus on learning one data science concept at a time and master it thoroughly, rather than getting an overview of multiple concepts at once. Let us start with a very fundamental question - Why is data crucial to businesses these days? What problems does it solve? All businesses, from a child’s lemonade stand to the largest corporations, must account for all their operations in order to be successful. This accounting of supplies, transactions, people, etc., is what we call ‘data’ and gives us historical records of what has transpired in a business. Without this data, we would be reduced to oral history or what humans used for accounting before the advent of writing systems. By collecting and analyzing data, we gain a deeper understanding of how the business is progressing. In the most basic instances, such as with a child’s lemonade stand, we know how many glasses of lemonade have been sold, how much was spent on supplies, and importantly whether the business is profitable. This example is incredibly trivial, but it should be noted that such simple data collection is not something that comes naturally to humans. For instance, many people have a desire to lose weight at some point in their life, but fail to accurately record their daily weight or calorie intake in any regular manner, despite the large number of free services available to help with this. There are so many Python-based libraries out there which can be used for a variety of data science tasks. Where does pandas fit into this picture? pandas is the most popular library to perform the most fundamental tasks of a data analysis. Not many libraries can claim to provide the power and flexibility of pandas for working with tabular data. How does pandas help data scientists in overcoming different challenges in data analysis? What advantages does it offer over domain-specific languages such as R? One of the best reasons to use pandas is because it is so popular. There are a tremendous amount of resources available for it, and an excellent database of questions and answers on StackOverflow. Because the community is so large, you can almost always get an immediate answer to your problem. Comparing pandas to R is difficult as R is an entire language that provides tools for a wide variety of tasks. Pandas is a single large Python library. Nearly all the tasks capable in pandas can be replicated with the right library in R. We would love to hear your journey as a data scientist. Did having a master's degree in statistics help you in choosing this profession? Also tell us something about how you leveraged analytics in professional Poker! My journey to becoming a “data scientist” began long before the term even existed. As a math undergrad, I found out about the actuarial profession, which appealed to me because of its meritocratic pathway to success. Because I wasn’t certain that I wanted to become an actuary, I entered a Ph.D. program in statistics in 2004, the same year that an online poker boom began. After a couple of unmotivating and half-hearted attempts at learning probability theory, I left the program with a masters degree to play poker professionally. Playing poker has been by far the most influential and beneficial resource for understanding real-world risk. Data scientists are in the business of making predictions and there’s no better way to understand the outcomes of predictions you make than by exposing yourself to risk. Your recently published 'pandas Cookbook' has received a very positive response from the readers. What problems in data analysis do you think this book solves? I worked extremely hard to make pandas Cookbook the best available book on the fundamentals of data analysis. The material was formulated by teaching dozens of classes and hundreds of students with my company Dunder Data and my meetup group Houston Data Science. Before getting to what makes a good data analysis, it’s important to understand the difference between the tools available to you and the theoretical concepts. Pandas is a tool and is not much different than a big toolbox in your garage. It is possible to master the syntax of pandas without actually knowing how to complete a thorough data analysis. This is like knowing how to use all the individual tools in your toolbox without knowing how to build anything useful, such as a house. Similarly, understanding theoretical concepts such as ‘split-apply-combine’ or ‘tidy data’ without knowing how to implement them with a specific tool will not get you very far. Thus, in order to make a good data analysis, you need to understand both the tools and the concepts. This is what pandas Cookbook attempts to provide. The syntax of pandas is learned together with common theoretical concepts using real-world datasets. Your readers loved the way you have structured the book and the kind of datasets, examples and functions you have chosen to showcase pandas in all its glory. Was is experience, intuition, or observations that led to this fantastic writing insight? The official pandas documentation is very thorough (well over 1,000 pages) but does not present the features as you would see them in a real data analysis. Most of the operations are shown in isolation on contrived or randomly generated data. In a typical data analysis, it is common for many pandas operations to be called one after another. The recipes in pandas Cookbook expose this pattern to the reader, which will help them when they are completing an actual data analysis. This is not meant to disparage the documentation as I have read it multiple times myself and recommend reading it along with pandas Cookbook. Quantitative finance is one domain where pandas finds major application. How does pandas help in developing better financial applications? In what other domains does pandas find important applications and how? Pandas has good time-series capabilities which makes it well-suited for financial applications. It’s ability to group by specific time periods is a very useful feature. In my opinion, pandas most important application is with exploratory data analysis. It is possible for an analyst to quickly use pandas to find interesting discoveries within the data and visualize the results with either matplotlib or Seaborn. This tight integration, coupled with the Jupyter Notebook interface make for an excellent ecosystem for generating and reporting results to others. Please tell us more about 'pandas Cookbook'. What in your opinion are the 3 major takeaways from it? Are there any prerequisites needed to get the most out of the book? The only prerequisite for pandas Cookbook is a fundamental understanding of the Python programming language. The recipes progress in difficulty from chapter to chapter and for those with no pandas experience, I would recommend reading it cover to cover. One of the major takeaways from the book is to be able to write modern and idiomatic pandas code. Pandas is a huge library and there are always multiple ways of completing each task. This is more of a negative than a positive as beginners notoriously write poorly written and inefficient code. Another takeaway is the ability to probe and investigate data until you find something interesting. Many of the recipes are written as if the reader is experiencing the discovery process alongside the author. There are occasional (and purposeful) missteps in some recipes to show how often the right course of action is not always known. Lastly, I wanted to teach common theoretical concepts of doing a data analysis while simultaneously learning pandas syntax. Finally, what advice would you have for beginners in data science? What things should they keep in mind while designing and developing their data science workflow? Are there any specific resources which they could refer to, apart from this book of course? For those just beginning their data science journey, I would suggest keeping their ‘universe small’. This means concentrating on as few things as possible. It is easy to get caught up with a feeling that you need to keep learning as much as possible. Mastering a few subjects is much better than having a cursory knowledge of many. If you found this interview to be intriguing, make sure you check out Ted’s pandas Cookbook which presents more than 90 unique recipes for effective scientific computation and data analysis.    

Data-driven testing has become a lot easier thanks to tools like Selenium. That's good news for everyone in software development. It means you can build better software that works for users much more quickly. While the tension between performance and the need to deliver will always remain, it's thanks to efforts of developers to improve testing tools that we are where we are today. We spoke to Carl Cocchiaro about data driven testing and much more. Carl is the author of Selenium Framework Design in Data-Driven Testing. He also talked to us about his book and why it's a useful resource for web developers interested in innovations in software testing today. What is data-driven testing? Packt: Tell us a little bit about data-driven testing. Carl Cocchiaro: Data-Driven Testing has been made very easy with technologies like Selenium and TestNG. Users can annotate test methods and add attributes like Data Providers and Groupings to them, allowing users to iterate through the methods with varying data sets. The key features Packt: What are the 3 key features of Selenium that makes it worth people's attention? CC: Platform independence, its support for multiple programming Languages, and its grid architecture that's really useful for remote testing. Packt: Could someone new to Java start using Selenium? Or are there other frameworks? CC: Selenium WebDriver is an API that can be called in Java to test the elements on a Browser or Mobile page. It is the Gold Standard in test automation, everyone should start out learning it, it's pretty fun to use. What are the main challenges of moving to Selenium? Packt: What are the main challenges someone might face when moving to the framework? CC: Like anything else, the language syntax has to be learned in order to be able to test the applications. Along with that, the TestNG framework coupled with Selenium has lots of features in Data-Driven Testing, and there's a learning curve on both. How to learn Selenium Packt: How is your book a stepping stone for a new Selenium developer? CC: The book details how to design and develop a Selenium Framework from scratch and how to build in Data-Driven Testing using TestNG and a Data Provider class. It's complex from the start but has all the essentials to create a great testing framework. They should get the basics down first before moving towards other types of testing like performance, REST API, and Mobile. Packt: What makes this book a must-have for anyone interested in or working with the tool? CC: Many Selenium guides are geared towards getting users up and running, but this is an advanced guide that teaches all the tricks and techniques I've learned over 30 years. Packt: Can you give people 3 reasons why they should read your book? CC: It's a must-read if designing and developing new frameworks, it circumvents all the mistakes users make in building frameworks, and you will be a Selenium Rockstar at your company after reading it! Learn more about software testing:  Unit Testing and End-To-End Testing Testing RESTful Web Services with Postman

Angular is one of the most popular JavaScript frameworks. Today, it's jostling for position with React and Vue as the leading front-end development framework. But if you're not using it, you're probably wondering why you should switch to Angular at all. What makes Angular such a good web development framework? And what does it give you that the likes of Vue and React don't? To help you decide if you should switch to Angular, we spoke to Minko Gechev, author of the third edition of Switching to Angular. Minko has worked incredibly closely with the Angular project - he was the author of the Angular 2 style guide. That means he's well placed to explain the benefits of Angular and to help you decide if its the right framework for you. Key Takeaways: Angular as against the public opinion has a consistent development lifecycle and is trustworthy for the developers to start their career with. The primary goal of the Angular community is to make the framework lighter and much more efficient. The book Switching to Angular - Third Edition works as a perfect stepping stone for new developers looking to start their journey with Angular. Angular.js and Angular are two different frameworks. Often people incorrectly refer to AngularJS as Angular version 1 which makes developers think that Angular is the next version of AngularJS. Although similar in nature, they should never be considered as predecessor and successor.   How has Angular come to dominate front-end development? What do you think is the primary reason for Angular to have the lion's share of front-end development market? Minko Gechev: There are several important reasons: Angular is a really well-designed framework which provides entire solution for the developers working on the user interface. The framework is developed and maintained by Google. This means that it is very well engineered and a high-quality solution. Angular provides great development experience. Since the beginning Angular was designed to simplify the development process dramatically. The framework provides friendly APIs which makes the process of building complex user interface trivial. Out of the box, Angular has very performant and well tested implementations of two-way data-binding, dependency injection and change detection. Notice that these will not only reduce the boilerplates in the process of application development but also make our code more testable, thanks to dependency injection. This means that the Angular team also thinks about improving the quality characteristics of our applications. On top of that Angular for web development provides a lot of external modules maintained by the Angular team itself, so the same team which works on Angular core, also develops Angular router, Angular forms, Angular material, etc. This allows us to confidently use the entire stack without bothering that the individual pieces of the puzzle may not fit well together. Another important fact is that the framework is developed by Google. This implies a high-quality engineering power for enterprises and individuals who can confidently bet on Angular as a long-term solution for development framework. As it is unlikely from Google to drop support for the framework unexpectedly. Keeping in mind that Angular is used in tonnes of projects inside Google itself, we can rest assured that it’s being tested on a very high-scale constantly. Google would never make a compromise in terms of quality. Development experience is something I am really interested in. The framework is developed with TypeScript. It provides great tooling support implemented in different text editors and IDEs; with precise auto completion suggestions and warning about typing errors; developers’ productivity can increase a lot. On top of that, Angular’s templates are very convenient for static analysis. This helps programmers to build tools which ease the development process even further. A few examples of such tools are codelyzer and the Angular Language Service. Finally, the framework provides predictable release schedule following semantic versioning. We always know what changes to expect in the next release to plan accordingly. What would you like to tell your followers about the latest development in the Angular eco-system? MG: Google is constantly making Angular for web development smaller and faster. Each new release introduces better tooling which drops more unused code from the bundles and makes them smaller. The Angular compiler also brings plenty of optimizations which transform our applications to better  performant versions of themselves. At the same time, we can see very minor incompatibility changes across major releases. The Angular team does an amazing job in making sure that they improve the framework a lot behind the surface but for us, the developers, they leave the APIs unchanged. On the other hand, we have the Angular community which builds on top of the core. There are plenty of community tools improving our day-to-day development process. Also, there are plenty of high quality material which help us to get started with the framework. Should beginners use Angular? Would it be advisable for a newbie JavaScript developer to start using Angular or would you suggest any other framework? MG: I’d definitely encourage beginner JavaScript developers to give Angular a try. They will build good habits by using TypeScript and have less bugs in their code because of the language’s type system. Also, Angular CLI is a great starting point for bootstrapping a new project - no matter whether you’re a beginner or an expert. What makes this book a must have for the would be Angular developers? MG: The book provides a gentle introduction to all the framework’s concepts. In the first chapters, we focus on ideas rather than digging into unfamiliar code. By the time we start writing applications, readers already have a good understanding of the framework foundation so they can just start coding without any struggle. As a former member of the Angular mobile team and long-term contributor, I have revealed implementation details which will help readers get in-depth knowledge about how everything works under the hood. From my teaching experience, I’ve noticed different patterns which help people develop a deeper understanding of new concepts and use them in practice. The main challenges of making the switch to Angular What are the main challenges that anyone would face while shifting to Angular framework? MG: There might be some conceptual overhead in the beginning. The framework encourages us to use best practices which means there might be a lot of new concepts, such as dependency injection, two-way data-binding, observables and Ahead-of-Time compilation. It takes time and practice to digest everything but the good thing is that we can do that incrementally. In the book I provide a step-by-step approach - we start developing fully functional applications with the bare minimum and after that increase our expressiveness and architecture by introducing new ideas and concepts. [box type="info" align="" class="" width=""]Check out this post to learn how to build components using Angular.[/box] Is Angular's dominance fragile? Angular is going through a lot of changes presently, do you feel that these many changes can impact the developer retainability of a particular tool or tech, especially when there are frameworks like React and Vue breathing down the neck of Angular? MG: The incorrect perception about fragility in the Angular’s API is a result of the rewrite of AngularJS. Often people incorrectly refer to AngularJS as Angular version 1 which makes developers think that Angular is the next version of AngularJS. This is incorrect. Angular and AngularJS are completely different frameworks, the only thing in common is that Angular is inspired by AngularJS. This incorrect statement also makes people think that the changes which will happen between Angular version 5 and Angular version 6, for instance, will be as impactful as the changes which happened between Angular version 2 and AngularJS. This is wrong as well. In fact, there were almost no backwards incompatible changes between Angular version 2 and Angular version 5 which means that the API is under strict control and the migration process between major releases is always only related to update of the project’s dependencies’ versions. Regarding the other libraries and frameworks out there such as React and Vue, I see that they are introducing a lot of new ideas as well. There’s competition which is very healthy since it allows the entire ecosystem to constantly evolve. 3 key features of Angular Please share three of the most remarkable features that you feel makes Angular special. Why should people switch to Angular? The dependency injection mechanism of the framework which makes our code testable and enforces better separation of concerns. I’ve had to use other alternative technologies in the past and I can honestly say that this is the feature I missed a lot. The Angular compiler is another remarkable feature. It transforms our application to much more performant versions of itself. Finally, I like the Angular templates. They have plenty of good features and thanks to the great design the Angular compiler is ableto perform a variety of optimizations over our applications. Could another framework replace Angular? Do you in the near future see any framework replacing Angular in popularity and if yes then why? MG: The JavaScript landscape has been very dynamic for the past couple of years. Although Vue is built by independent developers, recently there’s a noticeable focus on frameworks developed and maintained by large companies, such as React and Angular. I think this trend will stay because it’s much safer to bet the future of your product on a framework supported by a big organization. This means it is less likely to have unexpectedly discontinued maintenance and support. With all the qualities that Angular has, I believe that the framework has a very bright future. It has been adopted by a lot of large companies - Microsoft, Capital One, NBA, VMware, Google and many others. [box type="info" align="" class="" width=""]Check out this post on 5 web development tools that will matter in 2018[/box] When Angular made the breaking change to version 2, everyone was divided in their opinion about whether it was the right step, what was your thought? MG: As I mentioned above, Google didn’t introduce breaking changes to version 2 - they created a brand new framework inspired by AngularJS. It’s incorrect to refer to Angular as Angular 2, Angular 4, or Angular 5, etc. It’s just Angular. There’s a new major release of the framework every 6 months. At the moment we’re at version 5; 5 months from now Angular will reach version 6, etc. The differences between Angular version 2 and Angular version 5 are insignificant, there were very minimal backwards incompatible changes. It’s pointless to refer to every new version of Angular by placing a number behind unless we’re describing any specific change which happened across releases. In “Switching to Angular” I’ve focused on the solid foundation of the framework which is unlikely to change in near future. At ng-conf 2017 I introduced a tool which can automatically migrate any Angular application between versions 2 and 4 which simplified the transition process even further. On the other hand, AngularJS (which is incorrectly called Angular 1) is still in use, although Google strongly encourages developers to migrate to Angular. How is this book a stepping stone for a new Angular web developer and what should they try their hands on next? MG: “Switching to Angular” provides a great starting point for any intermediate JavaScript developer who wants to learn Angular. In case the reader has prior experience in AngularJS the transition of their mindset from AngularJS to Angular will be extremely smooth because of the comparison between the two frameworks that is provided. The book also explains in detail what should be the expectations of the readers regarding the future of the framework. If you are interested to get started with Angular javascript framework Switching to Angular - Third Edition is the go-to book to align with.  

Golang is currently one of the fastest growing programming languages in the software industry, finding its way into almost every nook and cranny of application development. Its speed, simplicity and reliability make it the perfect choice for all kinds of developers. We recently interviewed Mina Andrawos, an experienced Go engineer and the author of the book, Cloud Native programming with Golang.  Mina explains why Go is being rapidly adopted in various development areas and by leading projects like Docker and Ethereum, how it is evolving as a language and what makes it great for cloud development. He shares expert insights into Go’s adoption for mobile development, embedded systems and the serverless web. He has also thrown light on the new directions of cloud computing and how Go makes development a piece of cake. Author’s Bio Mina Andrawos is an experienced engineer who has developed deep expertise in Go by using it personally and professionally. He has written numerous Go applications with varying degrees of complexity. Other than Go, he has skills in Java, C#, Python, and C++. He has worked with various databases and software architectures and is skilled with the agile methodology for software development. Besides software development, he has working experience of scrum mastering, sales engineering, and software product management. Key Takeaways The 3 most notable features of Go are its concurrency model that sets it apart from mainstream languages, the fairly mature standard package which covers a wide range of use cases and its ease of deployment. Go is designed to be simple and intuitive, yet reliable and robust for application development. There are currently several mature tools to write Go programs, like VSCode, Vim, Atom or Sublime text. Mina’s book Cloud native programming with Golang helps you build production level cloud native microservices and covers a wide range of important topics in the space such as types of message queues, docker containers, how to monitor microservices, perform continuous integration and much more. Go can be viewed as a hybrid between mainstream statically typed languages like Java, and popular dynamic scripting languages like Javascript. Go was built with the goal of being fully cross-platform in mind, and it can work in smaller mobile processors like ARM. Full Interview Go is one of the most popular and fast growing programming languages. What according to you, are the 3 notable features of Go? Go is a very remarkable programming language. Numerous articles were written about the advantages of the language. Trying to gather notable features in Go can actually produce enough material to fill a number of white papers. However, having said that, let’s try to squeeze three out of them: 1. Concurrency: Go’s unique concurrency features are legendary. The language offers a concurrency model that stands apart from most mainstream programming languages. Go advocates a different way of thinking about concurrency problems in modern software. In one of the articles I wrote, I have described what concurrency means in the Go language. 2.  The Standard package: Go has the advantage of being coupled with a fairly mature standard package, which covers tons of key features for building modern software. This means that once you install Go, you can build production level software that can cover a wide range of use cases from Restful web APIs to encryption software, before needing to consider any third party packages. 3.  Ease of deployment: A program written in pure Go code typically compiles to a single native binary, which basically makes deploying an application written in Go as easy as copying the application file to the destination server. In other words, there is no special software needed to run Go applications in production servers like language runtimes \ virtual machines (As an example, for Java programs, we need to install the Java runtime environment in our production servers to run our programs) . Go is also cross platform, so you can target an operating system of your choice when compiling a piece of code. You have been developing software for quite some time now. What tools do you use on a day-to-day basis? Programming is a very fun craft, and the tools we use in our development are integral to making the environment enjoyable. For me, because I work with multiple programming languages, I use different tools based on the project. My current tool of choice for the Go programming language is VSCode, combined with its Go Plugin by lukehoban.  This is just my preference however. There are lots of other tools that could be used to write Go programs. Some developers prefer Vim with all it’s popular features, while others prefer Atom or sublime text. There is also a Go plugin for the IntelliJ IDE, which I had used in the past and really liked. What kind of learning plan would you suggest to web developers who are interested in using Go as their main development tool to build Cloud Native Applications? What aspects do you feel are tricky to get past? The plan would include three steps: Get comfortable with Go. Learn the design patterns, the software tools, and the technologies of cloud native applications. Get familiar with a cloud service provider (like AWS, Azure, or Google cloud) Go is designed from the grounds up to be simple and intuitive. This makes learning Go a better and more straight forward experience compared to many other languages. For developers new to Go, one of the best resources to start learning Go, is the Go tour. Once the developer is familiar with Go, then they are ready to move to the next step of the learning plan, which is to learn the design patterns of cloud native applications, as well as the software technologies needed to build and deploy such applications. A good way to start is to check out my newly published book: Cloud Native Programming with Go. One major advantage of the book is that it not only covers the technologies and design patterns associated with cloud native applications, but it also connects these technologies and design patterns with Go, which makes it an excellent resource for Go developers looking to build cloud native software. This, in my opinion, is the trickiest aspect a software professional needs to get past to acquire the necessary skills to build cloud native applications. For the third step, the execution will depend on the cloud service provider that you or your business would like to work with. Some enterprises like to utilize their own private clouds, while others are tied to a mainstream cloud provider due to existing contracts or executive preferences. For AWS, my book should provide enough insights into  how to write Go cloud native applications, that are capable of making use of the cloud platform. In the context of all the above, how does your book, Cloud Native programming with Golang, prepare its readers to be industry ready? What are the key takeaways for readers from your title and how does your book help with the learning curve? The book was the product of great amount of research, sleepless nights, and focused effort. I am a coauthor of the book with Martin Helmich, who I enjoyed working with immensely. The book was designed from the get go to expose the reader to the practical experience needed to build production level cloud native microservices in Go, with the least amount of fat. It takes the reader into an expanding learning journey, which starts from the ten thousands foot view of cloud native microservices, then dives deep down into all the different aspects that need to work together in harmony in order to produce production level cloud native applications. It will prepare you to be industry ready by covering a wide array of topics that are vital in a production environment. Examples include: Different types of message queues found in production environments, docker containers, monitoring microservices via Prometheus, continuous integration, Restful APIs design, security and authentication, AWS Go APIs, NoSQL databases, ReactJS, and more. What makes it so special that it doesn’t shy away from covering sophisticated and diverse topics from scratch. For example, if you look at the Restful API chapter, we don’t assume that you already have knowledge of the HTTP protocol or web services design. Instead, we build the concepts with you from point zero up. The only knowledge you need before reading the book is some familiarity with the Go language. Another example is our message queues chapter, you can start reading the chapter knowing nothing about message queues, but then finish the chapter with more than enough knowledge to be very effective in utilizing message queues in your applications. The book is perfect for readers who want to begin learning how to build cloud native microservice applications. It will carry the reader from a beginner level to a point where they become capable of tackling advanced tools and design patterns in that space. You've been working with several other languages like Java, C++, C# and Python. How does Go compare to the other languages you've worked with? Go, in my opinion, could be viewed as a hybrid between mainstream statically typed languages (like Java), and popular dynamic scripting languages (like Javascript). That is because Go doesn’t require the same level of verbosity that you would need in a Java program. However it’s still a bit more verbose than an equivalent Javascript or Python implementation, luckily, Go makes up for this extra verbosity compared to dynamic languages, by delivering software that is much faster than the equivalent Python or Javascript implementation. One very hotly debated feature that is missing in Go is generics. Some people in the community believe it’s a good thing Go doesn’t have generics, while others can’t wait till Go maintainers are convinced that generics need to be added. From my personal experience, I have come across situations where it would have been nice to have generics, however it never got to the point where I couldn’t complete the task at hand. Having said that, there are some situations where you can argue that a piece of Go code might be a bit more verbose than an equivalent piece of Java code that makes use of generics. As mentioned earlier, Go’s concurrency model is different than almost all mainstream programming languages. Once you master the building blocks of Go’s concurrency model (namely, Go channels and goroutines), you can build very powerful concurrent software with relative ease. I always find writing concurrent software in Go to be a much more smooth experience for me than writing concurrent software in other languages. Also another mention from earlier was the ease of deployment. I never tire from enjoying how easy it is to deploy my Go programs to production compared to other languages. One last notable mention is the tooling. Since Go is a relatively new programming language, the tooling is not yet as fancy as what’s available for older languages like C# or Java for example. However, having said that, the Go ecosystem is maturing nicely every day, and we have more than enough tools right now to build fairly sophisticated software in Go. There is no more proof of this fact than the uprise of advanced software projects written in Go like Docker and Ethereum. You've worked with JavaScript as well. What's your take on using Go for full-stack web development / Isomorphic web development, over JS? That is a very interesting question. For people not familiar with the term ‘isomorphic web development’, it basically means using the same programming language for most of the front-end and the back-end components of the web application (combined with CSS or LESS or some other front-end styling technology). There is an important distinction to make between ‘Isomorphic web development’, and ‘full-stack web development’. You can be a full-stack web developer, while using Javascript for the front-end in addition to another language like Go or Ruby for the backend. However, if you are building an ‘Isomorphic’ web application, the idea is that you make use of one language for almost all your code, whether it’s on the front-end or the backend. I think Go enjoys being in the sweet spot where simplicity meets performance. That is because, Go comes included with out-of-the-box packages, that make web development relatively smooth. Not to mention a growing third party ecosystem, that complements the standard package and further facilitates writing web applications in the Go programming language. Having said that, Javascript was built initially for the sole purpose of front-end web pages, but then grew in scope after the Node.Js project came into existence, which made Javascript a more than capable backend language as well. So for the sake of being neutral and impartial, I would like to cover some advantages and disadvantages of using Go for web development vs Javascript. Let’s start with the disadvantages of using Go for web development compared to Javascript: Javascript is a language that could natively be used in the frontend and the backend components of web applications, this will always be an advantage of using Javascript over any other programming language, when it comes to web development. However, in case of Go, this disadvantage is countered to some extent, by the existence of GopherJS . GopherJS converts Go code to Javascript code. This means that you can write front-end code in Go, then have it converted to Javascript in order to work on the browser, which will get you very close to the isomorphic web development experience you obtain from using Javascript on the frontend combined with Node.JS on the backend. GopherJS is a very popular project, with more than 6000 stars on Github. People use it and it delivers them results. Having said that, the disadvantage of GopherJS is that it’s not native, since it converts your Go code to Javascript code, which means that when tricky issues happen, you may need to troubleshoot the auto-generated Javascript code, which is not always a fun experience, especially if your reason for using GopherJS is to avoid Javascript in the first place. Your experience will vary based on your projects, and the goals you are trying to achieve. Where do you see the future of Go's development going? What changes or improvements can the community expect in future releases? Go is growing in popularity every day. I see an immensely positive outlook for the future of development in Go. I think the sky's the limit. Go currently powers some of the most exciting projects in the industry, like Docker, Kubernetes, and Ethereum, among many others. Not only that, but Go also became integral to the operations of major players in the software industry Like Google and Uber, among many others as well. All of this richness of the user base, provides Go unprecedented opportunities for growth and adoption. Engineers and maintainers who experienced Go first hand, tend to use it in their future endeavors, further enriching the ecosystem.  The language had been fairly stable and consistent for a while now, and no substantial language changes are to be expected in the near future. So if you start learning Go now, you skills will stay relevant for a long time. Most of the improvements currently getting added to Go are more related to it’s runtime performance as well as standard package enhancements. Are there any interesting areas of implementation you've noticed Go finding its way into? Do you think the language would be best fit for any specific kind of development? One interesting area for me that Go is starting to find it’s way into is mobile development. Since Go was built with the goal of being fully cross-platform in mind, it can work in smaller mobile processors like ARM for example. This means that programs written in Go not only can work in server and desktop operating systems -like Linux, MacOS, and Windows- but they also can function in mobile environments like Android and IOS.  Having said that, it is important to mention that the ecosystem for developing Go apps on mobile devices is still young and maturing. If curious, you can check https://github.com/golang/mobile for Go’s mobile tools. There is also an interesting Go framework that is still in early development but looks extremely promising as a tool to write mobile applications in Go, you can find it here: https://gomatcha.io Regarding best fit use cases for the language, I see Go as a powerhouse for backend software development. Especially the kind of modern backend that relies on microservices and distributed architectures. The power that Go gives you in the world of the server backend is indisputable. Can you give developers 3 reasons why they should pick up your book? This book Cloud Native programming with Golang covers a diverse set of practical topics from scratch, that can help the reader build production level cloud native microservices. We did a lot of research to put all these topics together. I honestly doubt you would find another resource that would cover all those topics in one place. Example of topics covered are: Restful APIs, Secure microservices, message queues (Kafka, RabbitMQ, and AWS SQS), ReactJS, MongoDB, DynamoDB, Docker, Kubernetes, AWS, microservices monitoring with Prometheus, and continuous delivery, among others. Additionally it covers the topics in a logical top-down order, which solidifies the learning process. So we start the journey by covering the 10,000 foot view about how a cloud native architecture looks like, the design, the thinking process, the scalability, and more. From there, we take satisfying deep dives into the different aspects of cloud native applications. Towards the end of the learning journey, we don’t just leave the reader with no direction. Instead, we offer a path forward to where they should take their learning journey to the next level. Amazon recently added Lambda support for Go. What's your opinion on Serverless Go? Would it go hand in hand with Cloud Native development? It was a very exciting announcement indeed. I believe serverless support is a powerful tool in the developer’s toolbox to build cloud native applications in Go. The option to include a serverless component in your application, allows you to automate very focused triggered tasks that are not supposed to run forever. This ability helps you build better cloud native applications in the long run. Microservices, on the other hand, are better suited for tasks and operations that are expected to run continuously. If you enjoyed this interview, do head over to check out Mina’s book, Cloud Native programming with Golang.

To celebrate the launch of Python Interviews, we ran a Q&A session on Twitter with some of the key contributors to the book. Author and interviewer Mike Driscoll (@driscollis), and experienced Python contributors Steve Holden (@holdenweb), and Alex Martelli (@aleaxit) got together to respond to your questions. Here's what happened... https://twitter.com/PacktPub/status/979055321959358465 https://twitter.com/aleaxit/status/979055993874104321 https://twitter.com/holdenweb/status/979056136199593984 https://twitter.com/driscollis/status/979056963987361793 The future of Python We then asked Mike, Steve and Alex what they thought the future of Python is going to look like. https://twitter.com/aleaxit/status/979057847660003328 https://twitter.com/holdenweb/status/979059669699309569 https://twitter.com/holdenweb/status/979059813459034112 https://twitter.com/driscollis/status/979059276017815554 How to get involved with the Python community We then asked what our experts think is the best way for someone new to the Python community to get involved. With the language growing at an immense rate, more people are (hopefully) going to want to contribute to the project. https://twitter.com/aleaxit/status/979059707389231105 https://twitter.com/holdenweb/status/979060708276137985 Advice for anyone new to programming Programmings popularity as a career choice is growing. That's not just true of new graduates but people looking to retrain and take on a new challenge in their career. But what should anyone new to programming know when starting out? https://twitter.com/aleaxit/status/979063034202107905 https://twitter.com/holdenweb/status/979061878554054658 https://twitter.com/driscollis/status/979061529575346177 Switching from Python 2.7 to Python 3 There's been considerable discussion within the community on the merits of shifting from Python 2.7 to Python 3. But whatever the obvious advantages are, there will always be resistance to change when it requires an investment of time and effort. And if you don't need to switch then why would you? Here's what Mike, Steve and Alex had to say... https://twitter.com/aleaxit/status/979063346665107457 https://twitter.com/holdenweb/status/979062974450192384 https://twitter.com/driscollis/status/979062547935571969 What gives Python an advantage over other programming languages? Why is Python so popular exactly? If it's growing at such a fantastic rate, why are developers and engineers turning to it? What does it have that other languages don't? https://twitter.com/aleaxit/status/979063792276471808 https://twitter.com/holdenweb/status/979064210608001025 https://twitter.com/driscollis/status/979063896173699072 Future Python releases If Python's going to remain popular, it's going to need to adapt and evolve with the needs of the developers of the future. So what capabilities and features would our experts like to see from Python in the future? https://twitter.com/driscollis/status/979064329864695813 https://twitter.com/aleaxit/status/979064880757063680 https://twitter.com/holdenweb/status/979064474496913408 What problems does Python face as a language? https://twitter.com/driscollis/status/979065953949552640 https://twitter.com/aleaxit/status/979065864539357184 https://twitter.com/holdenweb/status/979066065706725376 Why is Python so useful for AI and Machine learning? AI is a growing area that has expanded beyond the confines of data science into just about every corner of modern software engineering. Python has been a core part of this, and in part it explains part of the rise of Python's popularity - people want to build algorithms in a way that's relatively straightforward. https://twitter.com/driscollis/status/979066778771914752 https://twitter.com/holdenweb/status/979069094862389253 https://twitter.com/holdenweb/status/979069100831006721

Python is one of the best programming languages for machine learning, quickly coming to rival R's dominance in academia and research. But why is Python so popular in the machine learning world? Why is Python good for AI? Mike Driscoll spoke to five Python experts and machine learning community figures about why the language is so popular as part of the book Python Interviews. Programming is a social activity - Python's community has acknowledged this best Glyph Lefkowitz (@glyph), founder of Twisted, a Python network programming framework, awarded The PSF’s Community Service Award in 2017 AI is a bit of a catch-all term that tends to mean whatever the most advanced areas in current computer science research are. There was a time when the basic graph-traversal stuff that we take for granted was considered AI. At that time, Lisp was the big AI language, just because it was higher-level than average and easier for researchers to do quick prototypes with. I think Python has largely replaced it in the general sense because, in addition to being similarly high-level, it has an excellent third-party library ecosystem, and a great integration story for operating system facilities. Lispers will object, so I should make it clear that I'm not making a precise statement about Python's position in a hierarchy of expressiveness, just saying that both Python and Lisp are in the same class of language, with things like garbage collection, memory safety, modules, namespaces and high-level data structures. In the more specific sense of machine learning, which is what more people mean when they say AI these days, I think there are more specific answers. The existence of NumPy and its accompanying ecosystem allows for a very research-friendly mix of high-level stuff, with very high-performance number-crunching. Machine learning is nothing if not very intense number-crunching. "...Statisticians, astronomers, biologists, and business analysts have become Python programmers and have improved the tooling." The Python community's focus on providing friendly introductions and ecosystem support to non-programmers has really increased its adoption in the sister disciplines of data science and scientific computing. Countless working statisticians, astronomers, biologists, and business analysts have become Python programmers and have improved the tooling. Programming is fundamentally a social activity and Python's community has acknowledged this more than any other language except JavaScript. Machine learning is a particularly integration-heavy discipline, in the sense that any AI/machine learning system is going to need to ingest large amounts of data from real-world sources as training data, or system input, so Python's broad library ecosystem means that it is often well-positioned to access and transform that data. Python allows users to focus on real problems Marc-Andre Lemburg (@malemburg), co-founder of The PSF and CEO of eGenix Python is very easy to understand for scientists who are often not trained in computer science. It removes many of the complexities that you have to deal with, when trying to drive the external libraries that you need to perform research. After Numeric (now NumPy) started the development, the addition of IPython Notebooks (now Jupyter Notebooks), matplotlib, and many other tools to make things even more intuitive, Python has allowed scientists to mainly think about solutions to problems and not so much about the technology needed to drive these solutions. "Python is an ideal integration language which binds technologies together with ease." As in other areas, Python is an ideal integration language, which binds technologies together with ease. Python allows users to focus on the real problems, rather than spending time on implementation details. Apart from making things easier for the user, Python also shines as an ideal glue platform for the people who develop the low-level integrations with external libraries. This is mainly due to Python being very accessible via a nice and very complete C API. Python is really easy to use for math and stats-oriented people Sebastian Raschka (@rasbt), researcher and author of Python Machine Learning I think there are two main reasons, which are very related. The first reason is that Python is super easy to read and learn. I would argue that most people working in machine learning and AI want to focus on trying out their ideas in the most convenient way possible. The focus is on research and applications, and programming is just a tool to get you there. The more comfortable a programming language is to learn, the lower the entry barrier is for more math and stats-oriented people. Python is also super readable, which helps with keeping up-to-date with the status quo in machine learning and AI, for example, when reading through code implementations of algorithms and ideas. Trying new ideas in AI and machine learning often requires implementing relatively sophisticated algorithms and the more transparent the language, the easier it is to debug. The second main reason is that while Python is a very accessible language itself, we have a lot of great libraries on top of it that make our work easier. Nobody would like to spend their time on reimplementing basic algorithms from scratch (except in the context of studying machine learning and AI). The large number of Python libraries which exist, help us to focus on more exciting things than reinventing the wheel. Python is also an excellent wrapper language for working with more efficient C/C++ implementations of algorithms and CUDA/cuDNN, which is why existing machine learning and deep learning libraries run efficiently in Python. This is also super important for working in the fields of machine learning and AI. To summarize, I would say that Python is a great language that lets researchers and practitioners focus on machine learning and AI and provides less of a distraction than other languages. Python has so many features that are attractive for scientific computing Luciano Ramalho (@ramalhoorg) technical principal at ThoughtWorks and fellow of The PSF The most important and immediate reason is that the NumPy and SciPy libraries enable projects such as scikit-learn, which is currently almost a de facto standard tool for machine learning. The reason why NumPy, SciPy, scikit-learn, and so many other libraries were created in the first place is because Python has some features that make it very attractive for scientific computing. Python has a simple and consistent syntax which makes programming more accessible to people who are not software engineers. "Python benefits from a rich ecosystem of libraries for scientific computing." Another reason is operator overloading, which enables code that is readable and concise. Then there's Python's buffer protocol (PEP 3118), which is a standard for external libraries to interoperate efficiently with Python when processing array-like data structures. Finally, Python benefits from a rich ecosystem of libraries for scientific computing, which attracts more scientists and creates a virtuous cycle. Python is good for AI because it is strict and consistent Mike Bayer (@zzzeek), Senior Software Engineer at Red Hat and creator of SQLAlchemy What we're doing in that field is developing our math and algorithms. We're putting the algorithms that we definitely want to keep and optimize into libraries such as scikit-learn. Then we're continuing to iterate and share notes on how we organize and think about the data. A high-level scripting language is ideal for AI and machine learning, because we can quickly move things around and try again. The code that we create spends most of its lines on representing the actual math and data structures, not on boilerplate. A scripting language like Python is even better, because it is strict and consistent. Everyone can understand each other's Python code much better than they could in some other language that has confusing and inconsistent programming paradigms. The availability of tools like IPython notebook has made it possible to iterate and share our math and algorithms on a whole new level. Python emphasizes the core of the work that we're trying to do and completely minimizes everything else about how we give the computer instructions, which is how it should be. Automate whatever you don't need to be thinking about. Getting Started with Python and Machine Learning 4 ways to implement feature selection in Python for machine learning Is Python edging R out in the data science wars?

Charlotte Jee is a journalist specialising in technology and politics, currently working as editor for Techworld. Charlotte has founded, hosted and moderated a range of events including The Techies awards and her own ‘Women in Tech Speak Up’ event series. In 2017, Charlotte set up Jeneo with the mission to give women and underrepresented people a voice at Tech events. Jeneo helps companies source great individuals to speak at their events, and encourage event organisers to source panellists from a range of backgrounds. We spoke to Charlotte in a live Twitter Q&A to find out a bit more about Jeneo and Charlotte’s experience as a woman in the tech sector. Packt: Why did you decide to set up Jeneo? CJ: Good question! Basically, blame manels (aka all-male panels). I got tired of going to tech events and not seeing even one woman speaking. It's symptomatic of a wider issue with diversity within tech. And I resolved to start to do something about it! Packt: What have been your most interesting findings from the research you've carried out into women at tech events? CJ: Interestingly, the ratio of women to men speakers varies hugely across different events. However, promisingly, I've found that those who have worked on this specific issue have successfully upped their number of women speakers. The general finding was that from the top 60 London tech events, women comprise about a quarter of the speakers. The full research hasn't concluded just yet – so stay tuned. Packt: How has the industry changed since you started in Tech? Do you think it is getting easier for women to get into the Tech world? CJ: To be honest, I don't think that much progress has been made since I started working in tech. However, in the last year with scandals at Uber, Google and VC sexual harassment, the industry is starting to finally really focus on making itself more welcoming for women. Packt: What do you think is the best part of being a woman in the tech industry? CJ: I think the answer is the same regardless of your gender – the tech industry is at the forefront of the latest innovations within society and it is constantly changing, so you never get bored. Packt: What do you think is the worst part of being a woman in the tech industry? CJ: To be clear, I believe the majority of workplaces within tech are perfectly welcoming to women – however for the persistent minority that aren't, women can face all sorts of discrimination, both subtle and unsubtle. Packt: What advice would you give to a woman considering a career in the tech industry? CJ: Go for it! I honestly can't think of a better industry to work in. You can pretty much guarantee you'll be able to find work no matter what, so long as you keep your skills up to date. Packt: In what ways – positive or negative ­– do you think our education system influences the number of women in tech? CJ: Good question. I actually think that it's in education that this problem starts – from the experts I've spoken to, it seems like our education system too often seems to actively discourage women from pursuing careers in tech. Packt: What has been your biggest success in your tech career so far? CJ: I was really honoured when my boss promoted me to TechWorld News editor in 2015. Recently, it'd have to be the Women in Tech Speak Up event for 300 people I organised (single-handedly) in August 2017. Also, creating this list of 348 women working in tech in the UK which in many ways kicked this all off. Packt: What advice would you give to help tech companies to help increase their gender diversity? CJ: Way too much for one tweet here. Get senior buy-in, look carefully at your hiring process, be clear on culture/working practices, collect and publish data, provide flexible working (men want this too!) – if you want to more detail please get in touch with me. Packt: What do you think the future looks like for women working in tech? CJ: I feel more optimistic now than I have at any point. I think there is a huge amount of desire within the industry to be more inclusive. However, it's translating that goodwill into action – that's what I'll be focusing on. Packt: Are there any tech companies doing awesome things to increase diversity in their business? CJ: There's some impressive work from Monzo at the moment, who are highlighting the need for a diverse and inclusive team that represents their user base. There are plenty of others doing good work too, including Amazon UK. Packt: Are there any particular women in tech who have inspired you and who should we be following on Twitter? CJ:How long do you have? So many: @emercoleman @kitterati @ChiOnwurah @NAUREENK @annkempster @cathywhite10 @carrie_loves_ @JeniT @lily_dart @annashipman @yoditstanton That is truly just for starters. And of course, I have to add, the original woman in tech who inspired me is my Mum, Jane Jee (@janeajee) – she's CEO of RegTech startup Kompli-Global Compliance (@kompliglobal). Plus, obviously everyone on this list. Packt: Do you have any tips or advice for women to get on panels or bag speaking slots at tech events? CJ: Start small if possible, build up your conference – don't be afraid to put yourself out there! My research has found speaker submissions overwhelmingly come from men, get proactive and contact events you'd like to speak at. Thanks for chatting with us, Charlotte! Find out more about Jeneo here and follow Charlotte on Twitter: @CharlotteJee.