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We've been talking a lot about observability on the Packt Hub over the last few months. Back in March we spoke to Honeycomb CEO Charity Majors who told us why observability is so important and why it can be so challenging for engineering teams to implement. It's clear it's a big topic with plenty of perspectives - but one that could have a ripple effect across the software industry. To get a further perspective on the topic, we spoke to Yuri Shkuro, who's an engineer at Uber and author of Mastering Distributed Tracing (which was published in February) to talk about how distributed tracing can help engineers build more observable systems. Yuri spoke in detail in the podcast about the value of observability in the context of complex distributed systems, as well as some of the challenges in implementing distributed tracing. As one of the creators of Jaeger, an open source tool built specifically for distributed tracing, he's well-placed to comment on how the ecosystem is evolving and how organizations can start thinking more seriously about observability. Read an extract from Yuri's book here. The episode covers: The difference between monitoring and observability Some of the misconceptions around distributed tracing Who can benefit from distributed tracing - from DevOps to SREs Practical advice for getting started with distributed tracing Listen on SoundCloud: https://soundcloud.com/packt-podcasts/if-youre-on-call-you-need-observability-tools-uber-engineer-yuri-shkuro-on-distributed-tracing “Tracing is conceptually a white box instrumentation technique. You cannot do tracing in an application by purely observing it from the outside, because that feature of context propagation is simply not possible - if you have 10 incoming requests into an application concurrently, and it does 100 outbound requests then how do you know which ones correlate to the incoming requests? That’s what context propagation allows us to achieve, it allows us to establish causality within events.”

Back in February, Viktor Charypar, Tech Director at Red badger explained the benefits of using a monorepo. For many teams, especially those without the resources or a highly developed and well-supported engineering culture, the idea of a monorepo might sound a little strange - following on from this piece, I spoke to Viktor to get a little bit more detail on the benefits of a monorepo and why engineering teams should seriously consider using them. But I didn't just speak to him about monorepos - I was also interested in how Red Badger builds a forward thinking engineering culture that can empower its clients, and how the team embraces continuous learning to ensure everyone is on top of the trends and tools that are going to be impacting digital transformation in the future. So, let's take a look at what Viktor had to say... Why monorepos now? Richard Gall: Why a monorepo now? If you’re dealing with multiple microservices doesn’t it make sense to separate source code? Viktor Charypar: It would seem to make sense, but microservices architecture actually introduces a new level of complexity that needs to be managed, which monorepos make much easier. The main issue is in dependencies between the services and the contracts they agree to exchange data. If one side of the contract changes in an incompatible way without the other side adapting to that change, the system no longer works. This problem grows with the number of services in the system and so it’s especially prominent in microservices architectures. Managing each service’s source code in a separate repository makes it more difficult to understand its ties to the rest of the system. This forces you to adopt some kind of external versioning scheme, such as semantic versioning, to express which revisions of services work together as things change. These versions are decided by engineers manually as changes are made according to a set of rules, and then the dependent services are updated to refer to the latest version of the service they consume, when they are changed to be compatible. This is time-consuming and error-prone. In a monorepo, all the components of the system are versioned together and changes can be made across the system. This not only means an external versioning scheme is not required, but it also makes it easier to test and enforce contracts between services. Monorepos really come to their own when they are coupled with a Continuous Integration system aware of the dependencies between components in the repo. Given a change made by a developer and the knowledge of the dependency “graph”, we can deterministically decide which system components can be affected by the change and therefore need to be retested. It is then up to owners of each service to do a level of testing of their dependencies to make sure their behaviour didn’t change significantly enough to break their own functionality. All this is automated and can be executed without human intervention. Humans just make changes to the software and express expectations on their dependencies in terms of contracts and tests. Read next: Mozilla’s updated policies will ban extensions with obfuscated code Digital transformation challenges RG: What common problems are clients coming to you with? VC: In general, our clients recognise they need help with their digital product capabilities, i.e. delivering interesting propositions to their customers as digital products, typically websites and mobile apps. In large enterprise companies, this ability is generally predicated on going through a digital transformation - adopting agile delivery methods, breaking down functional silos and working in cross-discipline, vertically aligned teams that can decide things quickly and adapt to how customers respond to their product offering. Our clients typically come to us with one of a few problems ranging from needing help with product strategy, i.e. what to offer their customers and how to find which of the many ideas have a market fit. Through knowing what to do but struggling to deliver it at pace, all the way to already having a digital product offering, but one which doesn’t perform as expected. Either from the perspective of customer behaviour (e.g. low conversion rate) or from a technical quality perspective, i.e. the website is unstable, struggles under high load, there are long outages, etc. While our strength is traditionally in fast product delivery and quality, we can help across the board, from product strategy to what we call empower and embed - demonstrating how to deliver digital products quickly, sustainably and with high quality, helping to build internal capability and then handing over to them. Essentially we want to help our clients build sustainable businesses. Read next: Linux forms Urban Computing Foundation: Set of open source tools to build autonomous vehicles and smart infrastructure Learning and assessing new software and tools RG: How do you stay on top of new tools? Do you have a learning culture at Red Badger? VC: We absolutely do, from simple day to day things like all engineers being encouraged to pair program to learn from each other or everyone in the company having a yearly training budget as one of the benefits, to doing things like a yearly internal mini conference called Tech Lab for all the engineers to get together and share latest learnings and general experience from projects. We have actually recently published a report which started with an activity at the last Tech Lab, which answers a lot of the questions above. It’s available on our website here (and we’ll also follow it with a series of events). We also run a few regular meetups in London, the biggest being the London React Meetup, which we’ve been hosting regularly for about four years. RG: How do you assess tools? VC: There are a few things we generally look at. The first is obviously experimenting with the tool to work out what it does and how. We’re in a privileged position of starting new projects, often greenfield ones, fairly regularly. We typically use about 80% of tools we know and trust and about 20% of new ones, which we want to try out “in anger” and learn about. We also look at who is behind these tools, which are generally open source, and whether there is momentum behind them and support from the community. Open source software typically goes through a period of rapid innovation and competition in a certain area and then, eventually, the community settles on a few options that work the best and fit the different problems people are trying to solve. The future of open source - is it sustainable? RG: How do you see the future of open source - is it sustainable on its current model? VC: That’s an interesting thing to think about! It seems like the open source model is widely misunderstood as software being built by dedicated developers in their free time. But in reality, most large, popular open source projects are backed by large software companies and people maintain them as their day job - for example, Linux, Kubernetes, React. Even the web standards are set by standard bodies comprised of professionals supported by the major browser vendors. I think the model with a sole maintainer working on something in their spare time doesn’t really work if their project gets very popular and the demand on their time grows. We all know how people tend to behave on the internet and software industry is no exception, so maintainers who do it as a hobby are at a pretty high risk of burning out. For the major open source projects, this seems to be more of an exception, as they are typically maintained by a team of people employed by a company invested in the project. The sponsor benefits from the community contributions and, if the project gets popular, from controlling the direction of a de facto standard and the community benefits from someone else doing the lion’s share of the work. I look at it as being similar to science, where different people publicly contribute to push the boundaries of knowledge, just because pooling resources makes more sense and doesn’t stop any individual contributor from profiting on the results. In that sense, I think it’s a pretty sustainable model and leads to better quality, more versatile software.

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    

UX designer has become a popular job role with tech recruiters, anxious to give roles a little extra sparkle and some additional sex appeal. But has UX become inflated as a term? Is its value being diluted? Although paying close attention to the experience of users can only be a good thing, are we doing a disservice to the discipline by treating it as a buzzword or a fad? If we pretend something's sexy, how serious can we really be about it? Whatever the problems with the uses and abuses of UX today, a landscape characterized by dark patterns and digital detox is one that's certainly not that comfortable for users. That means UX design is arguably more important than ever. What UX design is... and what it isn't To get to the heart of what UX design is, as well as what it isn't, we spoke to Will Grant (@wgx) a UX Designer who has experience working with a range of clients on products that have found their way into the lives of millions of users around the world. Will is the author of 101 UX Principles, a definitive design guide that explores key issues in the field.  In the podcast episode, we discussed: What UX is and isn't The UX process - what UX designers actually do The key skills a UX designer needs Originality v. templating Whether developers need to write code What conversational UI means for UX Can good design really save the world? Or should we quit the bullshit? Listen here: https://soundcloud.com/packt-podcasts/can-good-design-really-save-the-world-will-grant-on-the-importance-of-ux-in-2019 Read next: Will Grant’s 10 commandments for effective UX Design

A lot of people rave about TensorFlow and Theano, but there are is one complaint you hear fairly regularly: that they can be a little challenging to use if you're directly building deep learning models. That’s where Keras comes to the rescue. It's a high-level deep learning library written in Python that can be used as a wrapper on top of TensorFlow or Theano, to simplify the model training process and to make the models more efficient. Sujit Pal is Technology Research Director at Elsevier Labs. He has been working with Keras for some time. He is an expert in Semantic Search, Natural Language Processing and Machine Learning. He's also the co-author of Deep Learning with Keras, which is why we spoke to him about why you should use start using Keras (he's very convincing). 5 reasons you should start using Keras Keras is easy to get started with if you’ve worked with Python before and have some basic knowledge of neural networks. It works on top of Theano and TensorFlow seamlessly to create efficient deep learning models. It offers just the right amount of abstraction - allowing you to focus on the problem at hand rather than worry about the complexity of using the framework. It is a handy tool to use if you’re looking to build models related to Computer Vision or Natural Language Processing. Keras is a very expressive framework that allows for rapid prototyping of models. Why I started using Keras Packt: Why did you start using using Keras? Sujit Pal: My first deep learning toolkit was actually Caffe, then TensorFlow, both for work related projects. I learned Keras for a personal project and I was impressed by the Goldilocks (i.e. just right) quality of the abstraction. Thinking at the layer level was far more convenient than having to think in terms of matrix multiplication that TensorFlow makes you do, and at the same time I liked the control I got from using a programming language (Python) as opposed to using JSON in Caffe. I've used Keras for multiple projects now. Packt: How has this experience been different from other frameworks and tools? What problems does it solve exclusively? Sujit: I think Keras has the right combination of simplicity and power. In addition, it allows you to run against either TensorFlow or Theano backends. I understand that it is being extended to support two other backends - CNTK and MXNet. The documentation on the Keras site is extremely good and the API itself (both the Sequential and Functional ones) are very intuitive. I personally took to it like a fish to water, and I have heard from quite a few other people that their experiences were very similar. What you need to know to start using Keras Packt: What are the prerequisites to learning Keras? And what aspects are tricky to learn? Sujit: I think you need to know some basic Python and have some idea about Neural Networks. I started with Neural Networks from the Google/edX course taught by Vincent Van Houke. It’s pretty basic (and taught using TensorFlow) but you can start building networks with Keras even with that kind of basic background. Also, if you have used numpy or scikit-learn, some of the API is easier to pick up because of the similarities. I think the one aspect I have had a few problems with is building custom layers. While there is some documentation that is just enough to get you started, I think Keras would be usable in many more situations if the documentation for the custom layers was better, maybe more in line with the rest of Keras. Things like how to signal that a layer supports masking or multiple tensors, debugging layers, etc. Packt: Why do you use Keras in your day-to-day programming and data science tasks? Sujit: I have spent most of last year working with Image classification and similarity, and I've used Keras to build most of my more recent models. This year I am hoping to do some work with NLP as it relates to images, such as generating image captions, etc. On the personal projects side, I have used Keras for building question answering and disease prediction models, both with data from Kaggle competitions. How Keras could be improved Packt: As a developer, what do you think are the areas of development for Keras as a library? Where do you struggle the most? Sujit: As I mentioned before, the Keras API is quite comprehensive and most of the time Keras is all you need to build networks, but occasionally you do hit its limits. So I think the biggest area of Keras that could be improved would be extensibility, using its backend interface. Another thing I am excited about is the contrib.keras package in TensorFlow, I think it might open up even more opportunity for customization, or at least the potential to maybe mix and match TensorFlow with Keras.

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

IntroductionIn the wild world of videogames, you'll inevitably encounter a foe that needs to be both engaging and captivating. This opponent isn't just a bunch of nice-to-see polygons and textures; it needs to be a challenge that'll keep your players hooked to the screen.Let's be honest, as a game developer, crafting a truly engaging opponent is often a challenge that rivals the one your players will face!In video games, we often use the term Artificial Intelligence (AI) to describe characters that are not controlled by the player, whether they are enemies or friendly entities. There are countless ways to develop compelling characters in video games. In this article, we'll explore one specific solution offered by Unreal Engine: behavior trees.NoteCitations come from my Artificial Intelligence in Unreal Engine 5 book.Using the Unreal Shooting Gym ProjectFor this article, I have created a dedicated project called Unreal Shooting Gym. You can freely download it from GitHub: https://github.com/marcosecchi/unreal-shooting-gym and open it up with Unreal Engine 5.4.Once opened, you should see a level showing a lab with a set of targets and a small robot armed with a gun (A.K.A. RoboGun), as shown in Figure 1: Figure 1. The project level.If you hit the Play button, you should notice the RoboGun rotating toward a target while shooting. Once the target has been hit, the RoboGun will start rotating towards another one. All this logic has been achieved through a behavior tree, so let’s see what it is all about.Behavior Trees“In the universe of game development, behavior trees are hierarchical structures that govern the decision-making processes of AI characters, determining their actions and reactions during gameplay.”Unreal Engine offers a solid framework for handling behavior trees based on two main elements: the blackboard and behavior tree assets.Blackboard Asset“In Unreal Engine, the Blackboard [...] acts as a memory space – some sort of brain – where AI agents can read and write data during their decision-making process.“By opening the AI project folder, you can double-click the BB_Robogun asset to open it. You will be presented with the blackboard that, as you can see from Figure 2, is quite simple to understand. Figure 2. The AI blackboardAs you can see there’s a couple of variables – called keys – that are used to store a reference to the actor owning the behavior tree – in this case, the RoboGun – and to the target object that will be used to rotate the RoboGun.Behavior Tree Asset“In Unreal Engine, behavior trees are assets that are edited in a similar way to Blueprints – that is, visually – by adding and linking a set of nodes with specific functionalities to form a behavior tree graph.”Now, double-click the BT_RoboGun asset located in the AI folder to open the behavior tree. You should see the tree structure depicted in Figure 3:Figure 3. The AI behavior treeAlthough this is a pretty simple behavior logic, there’s a lot of things involved here. First of all, you will notice that there is a Root node; this is where the behavior logic starts from.After that, you will see that there are three gray-colored nodes; these are defined composite nodes.“Composite nodes define the root of a branch and set the rules for its execution.”Each of them behaves differently, but it is sufficient to say that they control the subtree that will be executed; as an example, the Shoot Sequence node will execute all the subtrees one after the other.The purple-colored nodes are called tasks and they are basically the leaves of the tree, whose aim is to execute actions. Unreal Engine comes with some predefined tasks, but you will be able to create your own through Blueprints or C++.As an example, consider the Shoot task depicted in Figure 4: Figure 4. The Shoot task In this Blueprint, when the task is executed, it will call the Shoot method – by means of a ShootInterface – and then end the execution with success. For a slightly more complex task, please check the  BTTask_SeekTarget asset.Get back to the behavior tree, and you will notice that the Find Random Target node has a blue-colored section called Is Target Set? This is a decorator. “Decorators provide a way to add additional functionality or conditions to the execution of a portion of a behavior tree.”In our case, the decorator is checking if the TargetActor blackboard key is not set; the corresponding task will be executed only if that key is not set – that is, we have no viable target. If the target is set, this decorator will block task execution and the parent selector node – the Root Selector node – will execute the next subtree.Environment QueriesUnreal Engine provides an Environment Query System (EQS) framework that allows data collection about the virtual environment. AI agents will be able to make informed decisions based on the results.In our behavior tree, we are running an environment query to find a viable target in the Find Random Target task. The query I have created – called EQ_FindTarget – is pretty simple as it just queries the environment looking for instances of the class BP_Target, as shown in Figure 5:Figure 5. The environment queryPawn and ControllerOnce you have created your behavior tree, you will need to execute it through an AIController, the class that is used to possess pawns or characters in order to make them proper AI agents. In the Blueprints folder, you can double-click on the RoboGunController asset to check the pretty self-explanatory code depicted in Figure 6:Figure 6. The character controller codeAs you can see, it’s just a matter of running a behavior tree asset. Easy, isn’t it?If you open the BP_RoboGun asset, you will notice that, in the Details panel, I have set the AI Controller Class to the RoboGunController; this will make the RoboGun pawn be automatically possessed by the RoboGunController.ConclusionThis concludes this brief overview of the behavior tree system; I encourage you to explore the possibilities and more advanced features – such as writing your code the C++ way – by reading my new book “Artificial Intelligence in Unreal Engine 5”; I promise you it will be an informative and, sometimes, funny journey!Author BioMarco Secchi is a freelance game programmer who graduated in Computer Engineering at the Polytechnic University of Milan. He is currently lecturer of the BA in Creative Technologies and of the MA in Creative Media Production. He also mentors BA students in their final thesis projects. In his spare time, he reads (a lot), plays (less than he would like) and practices (to some extent) Crossfit.

What initially drew you to write your book for Packt Publishing? In 2009, I started writing technical articles on my personal blog. I would write about my field, Geographic Information Systems, or any other technical articles. Whenever a new technology emerged, a new product,or sometimes even mere tips or tricks,I would write an article about it. My blog became a well-known site in GIS, and that is when Packt approached me with a proposed title. I always wanted to write a book but I never expected that the opportunity would knock on my door. I thank Packt for giving me that opportunity. When you began writing, what were your main aims? My main aim was to write a book that readers in my domain could grab and benefit from. While working on a chapter, I would always imagine a reader picking up the book and reading that particular chapter and asked myself, what could I do better? And then I tried to make the chapter as simple as possible and leave nothing unexplained. What did you enjoy most and what was most rewarding about the experience of writing? Think about all the knowledge, information, ideas, and tips that you possess. You knew you had it in you somewhere but you didn’t know the joy and delight you would feel when this knowledge slipped through your fingertips into a physical medium. With each reading I would reread and polish the chapters;it seems there is always room for improvement in writing. Why, in your opinion, is ArcGIS exciting to discover, read, and write about? ArcGIS is not a new technology; it has been around for more than 14 years. It has become mature and polished during these years. It has expanded and started touching other bleeding-edge technologies like mobile, web, and the cloud. Everyday this technology is increasingly worth discovering and everyday it benefits areas like health, utilities, transportation, and so on. Why do you think interest in GIS is on the rise? If you read The Tipping Point,by Malcolm T. Gladwell, you will understand that the smartphone was actually a tipping point for the GIS technology. GIS was only used by enterprises and big companies who wanted to add the location dimension to their tabular data so it helped them better visualize and analyze their information. With smartphones and GPS, geographic location became more relevant. Pictures taken with smartphones are tagged with location information. Applications were developed to harness the power of GIS for routing, finding the best restaurants in an area, calculating shortest routes, finding information based on geo-fencing technology that sends you text messages when you pass by a shop, and so on. The popularity of GIS is rising and so is the interest in adapting this technology. What do you see on the horizon for GIS? High end processing servers are being sent to the cloud while we are carrying smaller and smaller gadgets. Networking is getting stronger every day with the LTE and 4G networks already setup in many countries. Storage has become no issue at all. The Web architecture is dominant so far and it is the most open and compatible platform that has ever existed. As long as we keep using devices, we will need geographic information systems. The data can be consumed and fetched swiftly from anywhere in the world from the smallest device. I believe this will evolve to an extent that everything valuable we own can be tagged with a location, so when we misplace something or lose it, we can always use GIS to locate it. Any tips for new authors? My role model author is Seth Godin; the first book I ever read was his. When I told him about my new book and asked him for any advice he might give me as a new author, he told me and I quote,″Congratulations, Hussein .This is thrilling to hear; my only advice is to keep writing!″ I took his advice and now I′m working on my second book with Packt. Another personal tip I can give to new authors is thatwriting needs focus, and I find music the best soul feeding source. While working on my first book,I discovered this site www.stereomood.com, which plays music that will help you write. Another thing is to use a clutter free word processor application that will blank the entire screen so you are only left with your words. I use WriteMonkey for Windows and Focus writer for Mac.

[dropcap]S[/dropcap]elf-service Business Intelligence is the buzzword everyone's talking about today. It gives modern business users the ability to find unique insights from their data without any hassle. Amidst a myriad of BI tools and platforms out there in the market, Microsoft’s Power BI has emerged as a powerful, all-encompassing BI solution - empowering users to tailor and manage Business Intelligence to suit their unique needs and scenarios. [author title="Brett Powell"]A Microsoft Power BI partner, and the founder and owner of Frontline Analytics LLC., a BI and analytics research and consulting firm. Brett has contributed to the design and development of Microsoft BI stack and Power BI solutions of diverse scale and complexity across the retail, manufacturing, financial, and services industries. He regularly blogs about the latest happenings in Microsoft BI and Power BI features at Insight Quest. He is also an organizer of the Boston BI User Group.[/author]   In this two part interview Brett talks about his new book, Microsoft Power BI Cookbook, and shares his insights and expertise in the area of BI and data analytics with a particular focus on Power BI. In part one, Brett shares his views on topics ranging from what it takes to be successful in the field of BI & data analytics to why he thinks Microsoft is going to lead the way in shaping the future of the BI landscape. In part two of the interview, he shares his expertise with us on the unique features that differentiate Power BI from other tools and platforms in the BI space. Key Takeaways Ease of deployment across multiple platforms, efficient data-driven insights, ease of use and support for a data-driven corporate culture are factors to consider while choosing a Business Intelligence solution for enterprises. Power BI leads in self-service BI because it’s the first Software-as-a-Service (SaaS) platform to offer ‘End User BI’ where anyone, not just a business analyst, can leverage powerful tools to obtain greater value from data. Microsoft Power BI has been identified as a leader in Gartner’s Magic Quadrant for BI and Analytics platforms, and provides a visually rich and easy to access interface that modern business users require. You can isolate report authoring from dataset development in Power BI, or quickly scale up or down a Power BI dataset as per your needs. Power BI is much more than just a tool for reports and dashboards. With a thorough understanding of the query and analytical engines of Power BI, users can customize more powerful and sustainable BI solutions. Part One Interview Excerpts - Power BI from a Bird’s Eye View On choosing the right BI solution for your enterprise needs What are some key criteria one must evaluate while choosing a BI solution for enterprises? How does Power BI fare against these criteria as compared with other leading solutions from IBM, Oracle and Qlikview? Enterprises require a platform which can be implemented on their terms and adapted to their evolving needs. For example, the platform must support on-premises, cloud, and hybrid deployments with seamless integration allowing organizations to both leverage on-premises assets as well as fully manage their cloud solution. Additionally, the platform must fully support both corporate business intelligence processes such as staged deployments across development and production environments as well as self-service tools which empower business teams to contribute to BI projects and a data driven corporate culture. Furthermore, enterprises must consider the commitment of the vendor to BI and analytics, the full cost of scaling and managing the solution, as well as the vendors’ vision for delivering emerging capabilities such as artificial intelligence and natural language. Microsoft Power BI has been identified as a leader in Gartner’s Magic Quadrant for BI and Analytics platforms based on both its currently ability to execute as well as its vision. Particularly now with Power BI Premium, the Power BI Report Server, and Power BI embedded offerings, Power BI truly offers organizations the ability to tailor and manage BI to their unique needs and scenarios. Power BI’s mobile application, available on all common platforms (iOS, Android) in addition to continued user experience improvements in the Power BI service provides a visually rich and common interface for the ‘anytime access’ that modern business users require. Additionally, since Power BI’s self-service authoring tool of Power BI Desktop shares the same engine as SQL Server Analysis Services, Power BI has a distinct advantage in enabling organizations to derive value from both self-service and corporate BI. The BI landscape is very competitive and other vendors such as Tableau and Qlikview have obtained significant market share. However, as organizations fully consider the features distinguishing the products in addition to the licensing structures and the integration with Microsoft Azure, Office 365, and common existing BI assets such as Excel and SQL Server Reporting Services and Analysis Services, they will (and are) increasingly concluding that Power BI provides a compelling value. On the future of BI and why Brett is betting on Microsoft to lead the way Self-service BI as a trend has become mainstream. How does Microsoft Power BI lead this trend? Where do you foresee the BI market heading next i.e., are there other trends we should watch out for?  Power BI leads in self-service BI because it’s the first software as a service (SaaS) platform to offer ‘End User BI’ in which anyone, not just a business analyst, can leverage powerful tools to obtain greater value from data. This ‘third wave’ of BI, as Microsoft suggests, further follows and supplements the first and second waves of BI in Corporate and self-service BI, respectively. For example, Power BI’s Q & A experience with natural language queries and integration with Cortana goes far beyond the traditional self-service process of an analyst finding field names and dragging and dropping items on a canvas to build a report. Additionally, an end user has the power of machine learning algorithms at their fingertips with features such as Quick Insights now built into Power BI Desktop. Furthermore, it’s critical to understand that Microsoft has a much larger vision for self-service BI than other vendors. Self-service BI is not exclusively the visualization layer over a corporate IT controlled data model – it’s also the ability for self-service solutions to be extended and migrated to corporate solutions as part of a complete BI strategy. Given their common underlying technologies, Microsoft is able to remove friction between corporate and self-service BI and allows organizations to manage modern, iterative BI project lifecycles.    On staying ahead of the curve in the data analytics & BI industry For someone just starting out in the data analytics and BI fields, what would your advice be? How can one keep up with the changes in this industry? I would focus on building a foundation in the areas which don’t change frequently such as math, statistics, and dimensional modeling. You don’t need to become a data scientist or a data warehouse architect to deliver great value to organizations but you do need to know the basic tools of storing and analysing data to answer business questions. To succeed in this industry over time you need to consistently invest in your skills in the areas and technologies relevant to your chosen path. You need to hold yourself accountable for becoming a better data professional and this can be accomplished by certification exams, authoring technical blogs, giving presentations, or simply taking notes from technical books and testing out tools and code on your machine. For hard skills I’d recommend standard SQL, relational database fundamentals, data warehouse architecture and dimensional model design, and at least a core knowledge of common data transformation processes and/or tools such as SQL Server Integration Services (SSIS) and SQL stored procedures. You’ll need to master an analytical language as well and for Microsoft BI projects that language is increasingly DAX. For soft skills, you need to move beyond simply looking for a list of requirements for your projects. You need to learn to become flexible and active – you need to become someone who offers ideas and looks to show value and consistently improve projects rather than just ‘deliver requirements’. You need to be able to have both a deeply technical conversation but also have a very practical conversation with business stakeholders. You need to able to build relationships with both business and IT. You don’t ever want to dominate or try to impress anyone but if you’re truly passionate about your work then this will be visible in how you speak about your projects and the positive energy you bring to work every day and to your ongoing personal development.   If you enjoyed this interview, check out Brett’s latest book, Microsoft Power BI Cookbook. In part two of the interview, Brett shares 5 Power BI features to watch out for, 7 reasons to choose Power BI to build enterprise solutions and more. Visit us tomorrow to read part two of the interview.

IntroductionThis article provides an in-depth exploration of memory allocation and deallocation in the .NET Common Language Runtime (CLR), covering essential concepts and mechanisms that every .NET developer should understand for optimal application performance. Starting with the fundamentals of stack and heap memory allocation, we delve into how the CLR manages different types of data and the roles these areas play in memory efficiency. We also examine the CLR’s generational garbage collection model, which is designed to handle short-lived and long-lived objects efficiently, minimizing resource waste and reducing memory fragmentation. To help developers apply these concepts practically, the article includes best practices for memory management, such as optimizing object creation, managing unmanaged resources with IDisposable, and leveraging profiling tools. This knowledge equips developers to write .NET applications that are not only memory-efficient but also maintainable and scalable.Understanding Memory Allocation and Deallocation in the .NET Common Language Runtime (CLR) Memory management is a cornerstone of software development, and in the .NET ecosystem, the Common Language Runtime (CLR) plays a pivotal role in how memory is allocated and deallocated. The CLR abstracts much of the complexity involved in memory management, enabling developers to focus more on building applications than managing resources.  Understanding how memory allocation and deallocation work under the hood can help you write more efficient and performant .NET applications. Memory Allocation in the CLR When you create objects in a .NET application, the CLR allocates memory. This process involves several key components, including the stack, heap, and garbage collector. In .NET, memory is allocated in two main areas: the stack and the heap. Stack Allocation: The stack is a Last-In-First-Out (LIFO) data structure for storing value types and method calls. Variables stored on the stack are automatically managed, meaning that when a method exits, all its local variables are popped off the stack, and the memory is reclaimed. This process is very efficient because the stack operates linearly and predictably. Heap Allocation: On the other hand, the heap is used for reference types (such as objects and arrays). Memory on the heap is allocated dynamically, meaning that the size and lifespan of objects are not known until runtime. When you create a new object, memory is allocated on the heap, and a reference to that memory is returned to the stack where the reference type variable is stored. When a .NET application starts, the CLR reserves a contiguous block of memory called the managed heap. This is where all reference-type objects are stored. The managed heap is divided into three generations (0, 1, and 2), which are part of the Garbage Collector (GC) strategy to optimize memory management: Generation 0: Short-lived objects are initially allocated here. This is typically where small and temporary objects reside. Generation 1: Acts as a buffer between short-lived and long-lived objects. Objects that survive a garbage collection in Generation 0 are promoted to Generation 1. Generation 2: Long-lived objects like static data reside here. Objects that survive multiple garbage collections are eventually moved to this generation. When a new object is created, the CLR checks the available space in Generation 0 and allocates memory for the object. If Generation 0 is full, the GC is triggered to reclaim memory by removing objects that are no longer in use. Memory Deallocation and Garbage Collection The CLR’s garbage collector is responsible for reclaiming memory by removing inaccessible objects in the application. Unlike manual memory management, where developers must explicitly free memory, the CLR automatically manages this through garbage collection, which simplifies memory management but requires an understanding of how and when this process occurs. Garbage collection in the CLR involves three main steps: Marking: The GC identifies all objects still in use by following references from the root objects (such as global and static references, local variables, and CPU registers). Any objects not reachable from these roots are considered garbage. Relocating: The GC then updates the references to the surviving objects to ensure that they point to the correct locations after compacting memory. Compacting: The memory occupied by the unreachable (garbage) objects is reclaimed, and the remaining objects are moved closer together in memory. This compaction step reduces fragmentation and makes future memory allocations more efficient. The CLR uses the generational approach to garbage collection in .NET, designed to optimize performance by reducing the amount of memory that needs to be examined and reclaimed.  Generation 0 collections occur frequently but are fast because most objects in this generation are short-lived and can be quickly reclaimed. Generation 1 collections are less frequent but handle objects that have survived at least one garbage collection. Generation 2 collections are the most comprehensive and involve long-lived objects that have survived multiple collections. These collections are slower and more resource-intensive. Best Practices for Managing Memory in .NET Understanding how the CLR handles memory allocation and deallocation can guide you in writing more efficient code. Here are a few best practices: Minimize the Creation of Large Objects: Large objects (greater than 85,000 bytes) are allocated in a special section of the heap called the Large Object Heap (LOH), which is not compacted due to the overhead associated with moving large blocks of memory. Large objects should be used judiciously because they are expensive to allocate and manage.  Use `IDisposable` and `using` Statements: Implementing the `IDisposable` interface and using `using` statements ensures that unmanaged resources are released promptly. Profile Your Applications: Regularly use profiling tools to monitor memory usage and identify potential memory leaks or inefficiencies. Conclusion Mastering memory management in .NET is essential for building high-performance, reliable applications. By understanding the intricacies of the CLR, garbage collection, and best practices in memory management, you can optimize your applications to run more efficiently and avoid common pitfalls like memory leaks and fragmentation. Effective .NET Memory Management, written by Trevoir Williams, is your essential guide to mastering the complexities of memory management in .NET programming. This comprehensive resource equates developers with the tools and techniques to build memory-efficient, high-performance applications.  The book delves into fundamental concepts like: Memory Allocation and Garbage Collection Memory profiling and Optimization Strategies  Low-level programming with Unsafe Code Through practical examples and best practices, you’ll learn how to prevent memory leaks, optimize resource usage, and enhance application scalability. Whether you’re developing desktop, web, or cloud-based applications, this book provides the insights you need to manage memory effectively and ensure your .NET applications run smoothly and efficiently. Author BioTrevoir Williams, a passionate software and system engineer from Jamaica, shares his extensive knowledge with students worldwide. Holding a Master’s degree in Computer Science with a focus on Software Development and multiple Microsoft Azure Certifications, his educational background is robust. His diverse experience includes software consulting, engineering, database development, cloud systems, server administration, and lecturing, reflecting his commitment to technological excellence and education. He is also a talented musician, showcasing his versatility. He has penned works like Microservices Design Patterns in .NET and Azure Integration Guide for Business. His practical approach to teaching helps students grasp both theory and real-world applications.

Security is an important aspect for organizations and securing the devices that contain confidential data--personal or professional, is absolutely essential. Microsoft Enterprise Mobility + Security, an intelligent mobility management and security platform, offers a suite of services that helps in securing employee devices; thus, protecting and securing the organization. Our recent chat with Nate Chamberlain, a Business Analyst at DH Pace, Kansas, helped us understand more about the Microsoft 365 Enterprise Mobility + Security suite. Nate is also a Microsoft MVP in Office apps and services. His recently published book Microsoft 365 Mobility and Security – Exam Guide MS-101, helps users to plan, deploy, and manage Microsoft Office 365 services and gain the skills required to pass the MS-101 exam. In this interview, Nate also shares his favorite services from the suite, the importance of using Shadow IT in a controlled manner while ensuring security Cloud Apps, how the M365 Certified Enterprise Administrator Expert certification has helped him give a career boost, and much more. On the Microsoft Enterprise Mobility and Security suite Microsoft Enterprise Mobility and Security suite help professionals secure devices used within the enterprise and also helps in identifying breaches before they cause any major damage. The suite provides two offerings, Enterprise Mobility + Security E3 and E5. Talking about the popular Microsoft solutions in the suite, Nate said, “Azure Active Directory, Intune, and the Microsoft 365 Security & Compliance Center are big players in the overall EM+S suite. Taking the time to get to know each of them has the potential to significantly enhance your organization’s security.” Nate said one of his favorite features from the Microsoft 365 Enterprise Mobility + Security suite is, “the ability to be extremely granular in building conditional access policies. That, paired with the ability to utilize AI and zero-day security information in policies and practices, continually impresses me. It’ll be interesting to see where Endpoint Manager takes us.” On the topic of what features he would like to add in the suite in the future, he said, “the biggest improvement I would hope for currently is licensing simplification, and making sure admins are able to secure their organization and its users without breaking the budget.” On the new Microsoft Endpoint Manager and using ‘Shadow IT’ for Cloud App security Last month, Microsoft announced its new Endpoint Manager, a convergence of two of its popular tools, System Center Configuration Manager (ConfigMgr) and Microsoft Intune. Both ConfigMgr and Intune offer integrated cloud-powered management tools, and unique co-management options to provision, deploy, manage, and secure endpoints and applications across an organization. The Endpoint Manager offers end-to-end management solutions without the need for worrying too much about the complexity involved during migration, thus helping customers in a smooth cloud transition. According to Nate, “Microsoft Endpoint Manager takes a lot of the licensing guesswork out of building a secure solution for your organization.”  In addition to Intune and ConfigMgr, Microsoft Endpoint Manager includes the Device Management Admin Center (DMAC) and Desktop Analytics. Nate further adds that Microsoft Endpoint Manager includes nearly everything discussed in his exam prep book, Microsoft 365 Mobility and Security – Exam Guide MS-101--including Intune and ConfigMgr. Shadow IT and cloud app security In his book, Nate has written about controlling the use of ‘Shadow IT’ for Cloud App security. Shadow IT, also known as Stealth IT, is built and used without the knowledge of the IT or security group within the organization. We asked Nate for what processes is Shadow IT built-in the organizations. We also asked why Shadow IT is a threat and how organizations can minimize its usage. Clearing the clouds on Shadow IT, Nate explains, “Shadow IT is often a consequence of being too restrictive without providing alternative means of productivity and collaboration solutions. And sometimes, even if you provide alternatives or company-licensed tools, it’s the lack of ongoing education that failed to spread awareness and competency that led users to more familiar, comfortable means of accomplishing goals. When users need to accomplish something, they’ll find a way with or without the organization’s assistance.” He further adds, “It’s IT administrators’ responsibility to make sure productivity and collaboration solutions are provisioned and configured for secure, appropriate usage, and that education is provided to get users on board.” A few key takeaways from Nate’s book, an MS-101 exam guide, and his recommendations for further Microsoft 365 certifications According to Microsoft’s official website, the “Exam MS-101: Microsoft 365 Mobility and Security”, the skills measured in the exam includes, implementing modern device services, implementing Microsoft 365 security and threat management, and managing Microsoft 365 governance and compliance. These skills would help companies sieve through all the candidates among others who don’t know much about the suite. Talking about the key takeaways from his book, Nate says, “I hope readers find the content to be challenging, but accessible. The best takeaway I could hope for is that readers retain information that not only helps them in the exam but in their jobs. The whole point of taking exams and obtaining certifications is to demonstrate proficiency, knowledge, and skill. Ultimately, it’s practising those skills in the real world that matter - not the score on the exam. But the exam is absolutely a first step toward building confidence and career growth.” We also asked him what other certifications he would recommend next, to which Nate said, “Once readers pass MS-101, they should aim for passing MS-100 if they haven’t already. After that, they’re just one prerequisite certification away from becoming a Microsoft 365 Enterprise Administrator Expert.” On Nate’s journey as a Microsoft SharePoint Systems Engineer and beyond Nate worked as a SharePoint Systems Engineer at LMH Health, Kansas, and is currently a Business Analyst at DH Pace in Olathe, KS. He is also an M365 Certified Enterprise Administrator Expert. He shared why certifications are important for career growth. He says, “My certification certainly looks great on my resume to potential employers and I like to think it’s part of what made me competitive in pursuing my current role. Certifications are verified proof of skill and competency. It alleviates some risk a company would otherwise assume in hiring someone for highly technical work like we find in our industry.” He also shared about his journey and how learning SharePoint transformed his role. “My journey has been one of self-teaching, fueled by inspiring tech solutions coming out of Microsoft. I was once tasked to learn what I can about SharePoint, at the University of Kansas, and it turned into a SharePoint-specific role there. That opened doors for me which brought me to LMH Health and ultimately DH Pace.” He continues, “Somewhere along the way, I started sharing what I was learning via my blog, NateChamberlain.com, and by speaking at conferences around the country regularly. I also started a SharePoint user group, LSPUG, in Lawrence, KS. For these reasons and perhaps others, I was awarded the Microsoft MVP for Office Apps and Services.” Certifications are indeed verified proof of skill and competency. So go ahead and check out Nate’s book, Microsoft 365 Mobility and Security – Exam Guide MS-101, to get up to speed with planning, deploying, and managing Microsoft Office 365 services and gain the skills you need to pass the MS-101 exam. With this book, you’ll explore everything from mobile device management and compliance, through to data governance and auditing. By the end of this book, you’ll have learned to work with Microsoft 365 services and covered the concepts and techniques you need to know to pass the MS-101 exam. Written in a succinct way, you’ll explore chapter-wise self-assessment questions, exam tips and mock exams with answers. Microsoft technology evangelist Matthew Weston on how Microsoft PowerApps is democratizing app development [Interview] How PyTorch is bridging the gap between research and production at Facebook: PyTorch team at F8 conference SOLIDWORKS specialist Tayseer Almattar takes us into the world of 3D modeling using SOLIDWORKS 2020 [Interview]

IntroductionTest-Driven Development (TDD) is a software development process widely used inAngular development to ensure code quality and reduce the time spent debugging. TDD is an agile approach to software development that emphasizes the iterative writing of tests before writing the actual code.In this article, we'll take a look at how to serenely build an application based on the Test-Driven Development approach.We'll start with a brief look at the process of developing an application without TDD, then use the Test-First approach to better understand the benefits of TDD, and finish with a case study on cart processing in an e-commerce application, for example.Developing an application without using Test-Driven Development? When we generally develop an application without using TDD, this is how we proceed:First, we write the source code;Next, we write the test corresponding to the source code;And finally, we refactor our code.As we evolve, we find it hard to detect and manage bugs in our application, because we always manage to write the tests to satisfy our code. As a result, we don't manage all the test cases we need to, and our application is very sensitive to the slightest modification. It's very easy to get regressions.In the next section, we'll look at how to develop an application using the Test-First approach.Developing a Test-First application? Developing an application using the Test-First approach means writing the tests before writing the source code, as the name indicates. Here's how we do it:Write the exhaustive tests related to the feature we want to implement;Then write the code associated with the tests;Finally, we refactor the code.Using this approach, we are constrained to have an exhaustive vision of the features to be developed on our product, which means we can't evolve in an agile context. If tomorrow's features need to evolve or be completely modified, we'll have a very difficult time updating our source code. Also, we'll be very slow in developing features, because we'll have to write all the possible test cases before we start writing the associated code.Now that we've seen these two approaches, in the next session we'll move on to the main topic of our article, the Test-Driven Development approach.Getting started with Test-Driven Development methodologyTest-Driven Development is a dynamic software development methodology that prioritizes the incremental creation of tests as minimalist code is implemented, while conforming to the User Story. In this section, we’ll explore the role of TDD in application development, then we’ll discover the core principle of TDD and the tools needed to successfully implement this approach.What role does TDD play in application development?TDD plays a crucial role in improving software quality, reducing bugs, and fostering better collaboration between developers and QA teams throughout the development process. We can summarize its role in three major points:Quality: TDD guarantees high test coverage, so that bugs can be detected quickly and high-quality code maintained.Design: By writing tests upstream, we think about the architecture of our code and obtain a clearer, more maintainable design.Agility: TDD favors incremental and iterative development, making it easier to adapt to changing needs.In this section, we’ll discover the core principle of TDD.What is the core principle of TDD?The TDD approach is based on a cycle known as the red-green-refactor cycle, which forms the core of this approach. Here's how it works:Write a test before coding: We start by defining a small piece of functionality and writing a test that will fail until that functionality is implemented. We don’t need to write the exhaustive tests related to the feature.Just the right minimalist code: We then write the minimum code necessary for the test to pass.Refactor: Once the test has been passed, the code is improved without changing its behavior.In the next section, we’ll learn the tools needed to successfully implement this approachTools for Test-Driven DevelopmentCase study: << Managing a shopping cart on a sales site. >>In this case study, we’ll implement some features about management of a shopping cart. The goal is very simple. Given a developer, your task is to develop the shopping cart of a sales site using a Test-Driven Development approach. We’ll do it step-by-step following the principles of TDD.When we’re asked to implement the shopping cart on an e-commerce application, we’ve to stay focused on the application and ask ourselves the right questions to find the most basic scenario when a customer lands on the shopping cart page. In this scenario, the shopping cart is empty by default when a customer launches your application. So, we’ll follow these steps about our first-ever scenario:Write the test on the assumption that the customer's shopping cart is empty at initialization (the most basic scenario):Write the minimal code associated with the test:Refactor the code if necessary:We don’t need a refactoring in our case.Now, we can continue with our second scenario. Once we’re satisfied that the basket is empty when the application is launched, we’ll check that the total cart price is 0. We’ll follow the principles of TDD and stay iterative on our code.Write the test to calculate the total basket price when the basket is empty:Write the minimal code associated with the test:Refactor the code if necessary.ConclusionTest-Driven Development (TDD) is a powerful methodology that enhances code quality, fosters maintainable design, and promotes agility in software development. By focusing on writing tests before code and iterating through the red-green-refactor cycle, developers can create robust, bug-resistant applications. In this article, we’ve explored the differences between traditional development approaches and TDD, delved into its core principles, and applied the methodology in a practical e-commerce case study.For a deeper dive into mastering TDD in Angular, consider reading Mastering Angular Test-Driven Development by Ezéchiel Amen AGBLA. With this book, you’ll learn the fundamentals of TDD and discover end-to-end testing using Protractor, Cypress, and Playwright. Moreover, you’ll improve your development process with Angular TDD best practices.Author BioEzéchiel Amen AGBLA is passionate about web and mobile development, with expertise in Angular and Ionic. A certified Ionic and Angular expert developer, he shares his knowledge through courses, articles, webinars, and training. He uses his free time to help beginners and spread knowledge by writing articles, including via Openclassrooms, the leading online university in the French-speaking world. He also helps fellow developers on a part-time basis with code reviews, technical choices, and bug fixes. His dedication to continuous learning and experience sharing contributes to his growth as a developerand mentor.

Introduction In the rapidly evolving world of computer graphics, Vulkan has emerged as a powerful and efficient API, revolutionizing how developers approach rendering and compute operations. As the author of "The Modern Vulkan Cookbook," I've had the privilege of diving deep into this technology, exploring its intricacies, and uncovering its potential to solve real-world problems in graphics programming. This book will help you leverage modern graphics programming techniques. You’ll cover a cohesive set of examples that use the same underlying API, discovering Vulkan concepts and their usage in real-world applications.Vulkan, introduced by the Khronos Group in 2016, was designed to address the limitations of older graphics APIs like OpenGL. Its low-overhead, cross-platform nature has made it increasingly popular among developers seeking to maximize performance and gain fine-grained control over GPU resources. One of Vulkan's key strengths lies in its ability to efficiently utilize modern multi-core CPUs and GPUs. By providing explicit control over synchronization and memory management, Vulkan allows developers to optimize their applications for specific hardware configurations, resulting in significant performance improvements. Vulkan Practical Applications Vulkan's impact on solving real-world problems in graphics programming is profound and far-reaching. In the realm of mobile gaming, Vulkan's efficient use of system resources has enabled developers to create console-quality graphics on smartphones, significantly enhancing the mobile gaming experience while conserving battery life. In scientific visualization, Vulkan's compute capabilities have accelerated complex simulations, allowing researchers to process and visualize large datasets in real-time, leading to breakthroughs in fields like climate modeling and molecular dynamics. The film industry has leveraged Vulkan's ray tracing capabilities to streamline pre-visualization processes, reducing production times and costs. In automotive design, Vulkan-powered rendering systems have enabled real-time, photorealistic visualizations of car interiors and exteriors, revolutionizing the design review process. Virtual reality applications built on Vulkan benefit from its low-latency characteristics, reducing motion sickness and improving overall user experience in training simulations for industries like healthcare and aerospace. These practical applications demonstrate Vulkan's versatility in solving diverse challenges across multiple sectors, from entertainment to scientific research and industrial design. Throughout my journey writing "The Modern Vulkan Cookbook," I encountered numerous scenarios where Vulkan's capabilities shine in solving practical challenges: GPU-Driven Rendering: Vulkan's support for compute shaders and indirect drawing commands enables developers to offload more work to the GPU, reducing CPU overhead and improving overall rendering efficiency. This is particularly beneficial for complex scenes with dynamic object counts or procedurally generated geometry. Advanced Lighting and Shading: Vulkan's flexibility in shader programming allows for the implementation of sophisticated lighting models and material systems. Techniques like physically based rendering (PBR) and global illumination become more accessible and performant under Vulkan. Order-Independent Transparency: Achieving correct transparency in real-time rendering has always been challenging. Vulkan's support for advanced rendering techniques, such as A-buffer implementations or depth peeling, provides developers with powerful tools to tackle this issue effectively. Ray Tracing: With the introduction of ray tracing extensions, Vulkan has opened new possibilities for photorealistic rendering in real-time applications. This has profound implications for industries beyond gaming, including architecture visualization and film production. Challenges and Learning Curves Despite its power, Vulkan comes with a steep learning curve. Its verbose nature and explicit control can be daunting for newcomers. During the writing process, I faced the challenge of breaking down complex concepts into digestible chunks without sacrificing depth. This led me to develop a structured approach, starting with core concepts and gradually building up to advanced techniques. One hurdle was explaining the intricacies of Vulkan's synchronization model. Unlike older APIs, Vulkan requires explicit synchronization, which can be a source of confusion and errors for many developers. To address this, I dedicated significant attention to explaining synchronization primitives and their proper usage, providing clear examples and best practices. The Future of Graphics Programming with Vulkan As we look to the future, Vulkan's role in graphics programming is set to grow even further. The API continues to evolve, with new extensions and features being added regularly. Some exciting areas of development include: Machine Learning Integration: The intersection of graphics and machine learning is becoming increasingly important. Vulkan's compute capabilities make it well-suited for implementing ML algorithms directly on the GPU, opening up possibilities for AI-enhanced rendering techniques. Extended Reality (XR): With the rising popularity of virtual and augmented reality, Vulkan's efficiency and low-latency characteristics make it an excellent choice for XR applications. The integration with OpenXR further solidifies its position in this space. Cross-Platform Development: As Vulkan matures, its cross-platform capabilities are becoming more robust. This is particularly valuable for developers targeting multiple platforms, from high-end PCs to mobile devices and consoles. Conclusion Writing "The Modern Vulkan Cookbook" has been an enlightening journey, deepening my appreciation for the power and flexibility of Vulkan. As graphics hardware continues to advance, APIs like Vulkan will play an increasingly crucial role in harnessing this power efficiently. For developers looking to push the boundaries of what's possible in real-time rendering, Vulkan offers a robust toolset. While the learning curve may be steep, the rewards in terms of performance, control, and cross-platform compatibility make it a worthy investment for any serious graphics programmer. Author Bio Preetish Kakkar is a highly experienced graphics engineer specializing in C++, OpenGL, WebGL, and Vulkan. He co-authored "The Modern Vulkan Cookbook" and has extensive experience developing rendering engines, including rasterization and ray-traced pipelines. Preetish has worked with various engines like Unity, Unreal, and Godot, and libraries such as bgfx. He has a deep understanding of the 3D graphics pipeline, virtual/augmented reality, physically based rendering, and ray tracing. 

Software security has been 'shifting left' in recent years. Thanks to movements like Agile and Dev(Sec)Ops, software developers are finding that they have to take more responsibility for the security of their code. By moving performance and security testing earlier in the development lifecycle it's much easier to identify and capture defects and issues. The reasons for this are largely rooted in the utter dominance of open source software and the increasingly distributed nature of the systems we're building. To put it bluntly, if our software is open, and loosely connected, the opportunity for systems to be exploited by malignant actors grows vastly. To tackle this we're starting to see a wealth of platforms and tools emerge that are trying to support developers embrace security as a fundamental part of the development process. One such platform is Semmle, a code analysis platform designed to help developers and engineers identify issues quickly. To find out more about Semmle - and the wider DevSecOps movement - we spoke to Chief Security Officer Fermin Serna in an edition of the Packt Podcast. He explained how Semmle works, what its trying to achieve, and placed it in the broader context of this 'shift left' that's quickly becoming a new reality for many engineers. Listen to the episode: https://soundcloud.com/packt-podcasts/we-need-to-democratize-security-how-semmle-is-improving-open-source-security   To learn more about Semmle, visit its website here. You can also follow Fermin Serna on Twitter: @fjserna. Read next:  5 reasons poor communication can sink DevSecOps How Chaos Engineering can help predict and prevent cyber-attacks preemptively

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