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

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

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

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

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

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

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

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

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

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

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

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

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

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

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