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Thoughtworks Holding, Inc. provides technology consultancy services in North America, the Asia Pacific, Europe, and Latin America. The company's consultancy services integrate strategy, design, and software engineering to enable enterprises and technology disruptors to thrive as modern digital businesses. It offers enterprise modernization, platforms, and cloud services, including modernization strategy and execution, digital platform strategy and engineering organization transformation, cloud modernization, cloud-native application support and evolution, and platform assessment and remediation; and customer experience, product, and design services comprising idea to market, customer experience strategy, product management transformation, and product design and delivery. The company also provides data and artificial intelligence services, such as data strategy, intelligent products, data platforms and data mesh, continuous delivery for machine learning, and data governance; and digital transformation and operations services, which include delivery transformation, digital fluency, organization transformation, value-driven portfolio management, technology strategy, executive advisory, and digital foundations training. It serves its clients in various industry verticals, including technology and business services; energy, public, and health services; retail and consumer; financial services and insurance; and automotive, travel, and transportation. The company was founded in 1993 and is headquartered in Chicago, Illinois.

ThoughtWorks

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<strong>Thoughtworks</strong> Holding Inc (TWKS) stock is down -66.75% over the last 12 months, and the average rating from Wall Street analysts is a Buy. InvestorsObserver’s proprietary ranking syst

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is a regular time to take a look at what experienced industry experts think might be the next wave of tools, practices, and technologies to consider or stop using. Thoughtworks’ Technology Radar I always look forward to adding many new “blips” to my list, or seeing which are growing in adoption since you last took a look. Version 27 is out now, and it continues some trends that the radar has been following for a while, such as “platform as a service” or forms of group programming. This round-up is far from comprehensive, but just the blips that appealed to me or that, I thought, might appeal to you. If any of them take your interest, or you have an opinion on anything I’ve featured, ! head over to your profile and let our readers know Mainstreaming of Machine Learning It really does feel like that in the past 6 months or so, machine learning went from something that mostly only developers and data scientists interacted with to something that the public had awareness of. There was announcement after announcement of another ML or AI-based tool that spat out some sort of media after you fed something into it, and we were swamped with thousands of strange-looking images and videos of celebrities. This also means that developers interested in experimenting with ML also have a lot more resources available to them in the shape of data sets and frameworks to use those data sets. Of relevance to experimenting developers is , a field of machine learning enabling a variety of always-on use-cases and targeting battery-operated devices. TinyML Another is , a method for creating ML pipelines that might be more familiar to developers from other backgrounds. feature stores This radar also covered ML-related tools for improving the developer experience of building and training models, for example, the practice of “federated machine learning” for aggregating model data across machines for processing or synthetic data for initial model testing. Continuous Observability Despite being a relatively new term, “observability” has rapidly grown from a loose collection of practices and standards for building a better picture of a running system to become a broader term to mean, well, to mean all sorts of things. The latest area of the developer workflow to receive insights is one that I am surprised took so long, observability for continuous integration and delivery pipelines. While CI and CD certainly bring benefits, processes frequently go wrong with very little insight into what until you try, try again. Now, a handful of existing observability vendors and open-source projects are bringing tracing-style visualizations of continuous processes to hopefully help you debug quite what was going on. Software Bill of Materials As developers lean increasingly on semi-anonymous containers and packages for their work, those focused on security and legalities looked on and wondered, “do we trust this?”. This concern has led to a rush to standardize and normalize (in enterprise, especially) a “software bill of materials”, often referred to by the comical acronym, SBOMs. While, in theory, “anyone” can dig into an open-source project and see what its components are, this is generally easier said than done and isn’t possible with closed-source projects at all. Recent high-profile vulnerabilities in software revealed how small dependencies can bring such large problems. While SBOMs could go a long way to help, I wonder how many companies will want to reveal their inner workings and be honest about them. Sustainable Coding This is a topic that I was inspired to cover myself after speaking with someone from Thoughtworks about their Carbon cloud footprint tool. The tendency to endlessly throw new frameworks, tools, and cloud frameworks at a problem has led to many of us forgetting that all of these lines of code actually run somewhere on something. And that leads to carbon emissions. Water motivated by altruistic reasons or financial, and an ecumenic downturn leads to cost savings, this is a topic more are talking and thinking about. Closely related to the environmental cost of projects and their infrastructure, and an option I’ve seen used by production teams is which estimates the cost impact of changes you make to Terraform definitions. infracost Responsibilities as Code Any SaaS company defines service-level indicators (SLIs) and service-level objectives (SLOs) that their clients can expect. However, how companies defined these have often been a bit haphazard and non-standard, and with so much else defined in codified pipelines, why not this fundamental component too? Several Observability providers now offer this option and of course, a group of people started a standard, . OpenSLO A No to Node I dabble in JavaScript and TypeScript and have noticed a handful of emerging runtime alternatives to Node recently. I am not sure what is wrong with Node, aside from being fairly old now, to warrant alternatives, but I guess competition is good 🤷‍♂️? is one newer alternative that I tried a few months ago, and I’ll admit I wasn’t really sure what benefits it brought me, but it uses WebKit’s JavaScriptCore instead of Chrome’s V8 engine. Bun Again, variety isn’t a bad thing. Bun is written in , which I had never heard of before, and it claims to be a “drop-in” C/C++ replacement. Zig Which leads nicely to… C+++ If Zig, Rust, and other languages are anything to go by, numerous people feel that C++ needs a more modern replacement. Google has thrown another option into the list of contenders with . Carbon The language is still in experimental stages but claims to be easier to understand and onboard with. Google has a tendency to spin up many experimental projects and then dump them again, so Thoughtworks’ suggestion is currently “wait and see”.

Developer Trends from Thoughtworks’ Technology Radar 27

Tendencias de desarrolladores del Radar de tecnología de Thoughtworks 27

Tendances des développeurs du radar technologique de Thoughtworks 27

थॉटवर्क्स के टेक्नोलॉजी रडार 27 से डेवलपर रुझान

ソートワークスのテクノロジー レーダー 27 からの開発者トレンド

Tendências de desenvolvedores do Technology Radar 27 da Thoughtworks

Xu hướng dành cho nhà phát triển từ Radar công nghệ của Thoughtworks 27

Thoughtworks 技术雷达的开发者趋势 27

A while ago released an for anyone to host their own personal radar. ThoughtWorks open source repository technology I was curious to see how the reputable company implemented it and strangely I noticed a lot of concepts that reminded me of Java, such as and error handling with . stateful objects try/catch/exception All the application really does is take a single input and produce a tech radar. A Java-esque approach seemed inappropriate for that use case so I decided to rewrite some of it in ! Elm Step 1: Model the radar Let’s start things off by thinking about how to model the radar itself. If we model our problem carefully it can simplify the work ahead of us. First let’s consider what we know about the data. The radar is split into and . These quadrants and rings represent different categories that several can be placed into. four quadrants four rings blips Since we know there are only four quadrants and rings let’s represent those as . union types So far so good. Next up, the blips. Modeling the blips requires some extra thinking because there are two representations of a blip. The first representation is the blip data from the Google Sheet ( ). The information included is the , , , an flag, and a . example name ring quadrant isNew description The blips that are visualized on the radar need those same 5 properties but in order to be displayed on the radar they also need an . x-y position Several ways to model these blips include: This option uses fewer types but will inevitably lead to more . Throw all properties into a single record and make the type of the position field a . Maybe maybe handling This option removes the but at the cost of in two separate record fields. Treat the blips from the Google Sheet as a distinct type and record from the blips visualized on the radar. maybe verbosity duplicating data Since the properties of the radar blip are a superset of the blips from the Google Sheet we can . A blip positioned on the radar will have the Google Sheet blip data in addition to a defined position. represent the data with composition There are other options worth considering, especially options that leverage union types, but the third option will suffice for now and we can refactor later if needed. Next up is visualizing the radar. If you’re curious on how we’re doing in comparison to ThoughtWorks’ open source example then swing on over to their OO equivalent modeling . Step 2: Visualize the radar Now that we have the basic building blocks to construct a view let’s start building the radar visualization. The first thing I want to do is get the quadrants and rings visualized. We’ll heavily use SVG and borrow a lot of the styles from the ThoughtWorks open source project. I’m sure they won’t mind. Creating an arc The radar isn’t a circle. It’s technically four quarter-circles put together, a.k.a arcs. Circles in SVG are not that hard. Arcs, on the other hand, suck big time. Here’s a demonstration of what I mean. With the above code working it will be exiled to a utility module so that we don’t have to think about the implementation details for creating an arc in SVG. Visualizing quadrants Visualizing a quadrant only requires that we visualize 4 arcs (rings), each with a different radius. Not bad. If we do this three more times and provide different numbers we get the entire radar background. Visualizing blips There are two types of blips that can be visualized which is determined by the flag. One is a rounded triangle and the other resembles a smudged circle. isNew I’m going to simply transcribe ThoughtWorks’ for these blip visualizations to Elm. code ..more SVG implementation details that we’re not going to look at ever again. Next up, visualizing mocked blips on the radar. Step 3: Wire up the view with mock data Before we consume actual data from a Google Sheet we’re going to mock up some data to make sure our blips are correctly displayed on the radar. The above function is but it will produce 16 blips on the radar, one in each ring in each quadrant. However, this brings up the question of where exactly to position the blips. temporary I went ahead and peeked at how . In short, they produce a sinusoidal curve that a blip is to be positioned on and if it collides with another blip it will try another random position on this curve. ThoughtWorks did it We can do the same thing in Elm but it brings up an interesting topic.. how to do randomness in a pure functional language. <quick detour> Pure randomness with generators In Elm, every function is a pure function. In order to produce randomness we can ask Elm to generate us a random value for a given seed. A random value and another seed is returned so that we can use that seed to ask for yet more random values. However, if we’re producing many random values then managing these seeds becomes extremely tedious and prone to mistakes. Fortunately, Elm gives us the concept of a . According to its documentation, Generator a generator is like a recipe for generating certain random values. Here’s an example generator that can be used to produce random integers from 3 to 7. Random.int 3 7 Here’s another generator for producing a random list of even values under 100. Simply creating a generator doesn’t actually produce a random value. It still has to be passed to . Random.step (randomEvens, nextSeed) =Random.step (generateEvenValues 10) (Random.initialSeed 123) Given this knowledge we can compose a few generators to randomly position blips along a sinusoidal curve and keep producing values if there is a collision. This will behave identically to the ThoughtWorks algorithm and it also happens to be where we convert the to . List GoogleSheetBlip List PositionedBlip Randomly positioned blips with Generators I realize we went from 0 to 60 with generators in about 90 seconds. If the above code is not immediately understandable, don’t worry. Like much with Elm, it is initially unfamiliar but it very quickly becomes second nature to read that code. </quick-detour> Blips visualized with randomness When we run the mock data at the beginning of this section into our view function we get a radar with blips on it. At this point we can keep throwing data at the radar and verify that the algorithm for determining blip positions is working. The positioning here seems reasonable. Moving on. If you’re tracking progress here’s how ThoughtWorks is doing the same . It’s also worth noting that the radar is mirrored along the y-axis because we hard coded the random seed. That’s fine. Step 4: Model the app state Just as before, modeling the app state is incredibly important. When writing your own Elm applications it’s worth pausing, discussing, and considering the trade-offs for how to model your data. For this application we already have a radar but we’re missing a page to enter a Google Sheet url. I chose to model that page state like this: It will have a form with an initially empty url. The form might have an error and we’ll want to know if we’re actively retrieving the Google Sheet. There are to this approach, but we’ll stick with these three properties for now. alternatives That covers what I’m calling the . Lastly, there’s the that needs to be provided to The Elm Architecture. Landing Page Model This is loosely based on except with a few things cut out for the sake of simplicity, such as not dealing with routes for such a small application. Richard Feldman’s SPA example It’s important to point out that at a high level this application is in one of two states: We’re on the landing page and interacting with the url input form We’re on the radar page interacting with a tech radar Step 5: User input, regex, data retrieval, and error handling The landing page involves , , , , and in all of those cases. I’m going to skip the user input part because if you made it this far you know how to do that already. receiving input from the user parsing out a Google Sheet ID performing an HTTP request parsing the response handling errors If I’m wrong and handling user input in Elm is new to you I suggest pausing and checking out this example . Regular expressions in Elm We’re about to go 0 to 60 again. Hang on. The user will supply us with a link to their published Google Sheet, such as “ ”. https://docs.google.com/spreadsheets/d/11Fd0lwNIEUs2ymxNEiTfpM5CoQHQbMuOId8TjrQHDn0/edit?usp=sharing Hidden within this URL is an ID that we need to perform an HTTP request. It’s that unreadable string between “/d/” and “/edit?”. First we’ll declare a regular expression that will find that ID. It goes without saying that credit for this regex goes to ThoughtWorks again. Within that regex are groups and we care about the first group specifically. In order to apply a regex to a string we use the function. find matches =find All sheetIdRegex url That returns a list of matches that take the following shape. Our Google Sheet ID is hidden within the list. So what we want to do is get the first match in the list of matches returned from , and on that first match find the first item in its . submatches find submatches If you’ve worked with Elm lists and before you’re probably already aware that we’re about to go head first into . Thanks to the code to pluck off that ID and produce a Result isn’t too bad. List.head MaybeLand elm-community/maybe-extra The first collapses the double Maybe that occurs when you do a on a List of Maybes. However, it’s still a Maybe returned from so the second collapses it one more time before converting it to a Result. MaybeExtra.join List.head Maybe.map MaybeExtra.join Retrieving the Google Sheet data Once we have the ID we can make a request to retrieve the Google Sheet data in CSV format. When this Cmd is performed it’ll call with the Result. httpResultToMsg The above function does the following On success, split the string by new lines Iterate over each row and attempt to parse the CSV as a blip Successfully parsing data puts it in the list while parse errors are added to the list. blips errors Send off a Msg with the blips and any errors Parsing each CSV row is as simple as attempting to map the data onto our data model defined earlier. Conclusion This post is getting long so I’ll wrap things up by making a few observations in rewriting some of the ThoughtWorks Tech Radar. The BYO radar project used for , for graphics and HTML templating, and for Google Sheets. It uses to model its data and incorporated rather than gracefully handling errors. chance randomness d3 tabletop parsing stateful object instances exception handling The code is fairly complicated and even with understanding the desired behavior it’s difficult to track the implementation details. Most of all, after spending so much time in that code I still don’t understand what much of the code does. On the other hand, a functional approach with Elm yielded more comprehensible code with similar functionality. In fact, once you grasp the way Elm does randomness and regular expressions you’ll realize the code is much more simple too. To see the partial rewrite in action check out these two links: http://ckoster22.github.io/elmradar/ https://github.com/ckoster22/techRadar/tree/blogpost

Rewriting the ThoughtWorks Tech Radar in Elm

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Does the team or app size affect the release process? Well, it depends. Let’s imagine a startup with one small team. In this case, the team usually makes a feature, and then just releases it. Now, let’s imagine a large project, for example, a banking app, with many teams working on it. In this case, there should probably be a process, release cycles, and maybe some bureaucracy. Without that, there will be chaos. So, when does it become clear that it’s time to set up such a process for your app? In this article, I’ll share my experience of implementing Release Train for the Dodo Pizza app (Android and iOS) and the problems we faced that made our team implement Release Train. If you are the Team Lead/Tech Lead of an Android or iOS project that is growing fast and you have not managed the release process yet, I hope that our experience will help you. How It Used to Be In 2021, we were already using a Trunk-based Development (TBD) approach in our teams. We covered the code with feature toggles, decomposed tasks, and ran unit and UI tests. Our feature branches didn’t live long, and we had CI working. The release process was very simple: whoever was ready to roll their feature out, rolled it. Ideal Scenario Here’s roughly what our branch workflow looked like. Several teams (gray, blue, orange, and green) worked on different features. Since we were working according to TBD, each feature could live through several consecutive branches. For example, the gray team made their feature in 4 steps, the blue and orange teams made theirs in 1 step, and the green team made theirs in 2 steps. When a team finished a feature, they could roll out a release. For example, if the blue team finished a feature, they could do a release. Then, the orange team would finish a feature and do another release. We had the perfect flow, as it seemed then. It worked great up to a point, but all good things come to an end. Something went wrong: tough, crowded, and unpredictable Mammoth The first problem we encountered was that releases started accumulating a lot of features and became too big. The teams didn’t always want to release their features right away. The release and regression process was time-consuming and took 3–4 days. So if your feature was small and not urgent, you didn’t always get to release it yourself because probably, some other team would do a release soon, and it would be included in that release. Roughly it looked like this: This was quite a fragile arrangement, especially when the number of teams started to grow. Many teams developed many small features, and the total volume of new code in each new release became huge. When someone got to release their big feature, they had to release a whole together with it. mammoth The mammoth-releases resulted in: delayed regression; higher risk of regression bugs; higher risk of getting a bug in production. We needed to make it so that even if the blue and orange team from the example didn’t want to release, the release would be done somehow. Bottlenecks Every team is unique, and every feature is different. Sometimes, things happened in such a way that several teams would finish their features around the same time. In this case, there was a lot of “wait for me, I’ll merge it tomorrow morning, I promise!” going around. In the end, such bottlenecks resulted in: releases turning into mammoths; delayed releases negatively affecting the releasing team’s plans especially if everybody else’s needs were met. We needed to make 2 crucial changes: The releasing team shouldn’t need to wait for anyone; every other team should know when the next release is expected. Lack of Predictability Imagine, the blue team made a small feature and expected the orange team to release it soon. But something went wrong, and the orange team didn’t roll out the release either because of some problems of their own. As a result, the blue team told the business that the feature would be in production soon, but it turned out to be not soon enough. As a result, it is impossible to predict when the feature will be in production. This doesn’t mean that the blue team is irresponsible. If they have a super important or urgent feature, then of course they will release it themselves. But in other cases, there is no way to tell exactly when the feature will be available to the users. As you can guess, we were experiencing such issues quite often. We needed to be able to tell exactly when customers would get a feature in production regardless of its size or urgency. All 3 problems (mammoth releases, bottlenecks, and lack of predictability) are closely related and complement each other. However, probably the most fundamental and important of them all is the lack of predictability. It generates other problems. Release Train We've had enough; it was time to make a change. The was supposed to help us do that. Release Train The term Release Train means different things: a , or a that manages the release process. Here, we’re going to talk about the scheduled release process. scheduled release process dedicated team I like the way Release Train is described by Martin Fowler in the “ ” article, and the definition given by (maybe it belongs to Martin as well). Patterns for Managing Source Code Branches Thoughtworks in their tech radar This is how we have defined Release Train for ourselves: Release Train is the process of coordinating releases between teams. All releases happen on a fixed schedule, independent of whether the features are ready or not. The train doesn't wait for anyone. If you are late, you have to wait for the next one. Let's break it down with a couple of examples using our color-coded teams. Solving the Mammoth Problem Release Train happens on schedule and does not depend on who has merged what into the main branch. In the example below, the features from the blue and orange teams will be released. The rest will wait for the next train. We could wait a bit longer, but then we'd get a mammoth. Resolving Bottlenecks At the same time, the Release Train helps us plan our work more efficiently. Let’s say that the blue team originally planned to finish a feature later. But since everyone knows the release date, they can slightly rearrange their plans to finish the feature early. Or, on the contrary, they can realize that they will definitely not be on time for the next train, and hence, they can finish the feature safely because they know the whole schedule. In the example below, the blue team wanted to get to the release and merge all their changes before the release. Otherwise, there might have been a bottleneck. Most importantly, Release Train . gave us predictability by design These examples may seem obvious to some, but we solved problems as they arose. When there were no problems with releases, we didn’t bother using Release Train. When problems accumulated, we realized that the time had come. How to Implement the Release Train in Your Team The first thing we did was write an . An RFC refers to both the process itself and the design document that use before they start working on a project. Some use RFCs specifically, some use ADRs, and some just call them by the more generic term Design Doc. RFC many companies At Dodo Engineering, we use both RFCs and ADRs. Our RFC process looked like this: We drafted an RFC document; we discussed it in a small group, collected comments, and made adjustments; then the RFC was communicated to a wider group; then we implemented it; after that, we gathered feedback, tracked metrics, and evaluated results. The structure of the RFC document for our Release Train was as follows: description of the Release Train process; what teams are involved, what they are doing; what the schedule will be; metrics. In drafting the RFC, we relied on the experience of other companies: SkyScanner Soundcloud Monzo Facebook First Implementation First, we came up with this process: release every week; create a release branch on Wednesday morning; complete regress, and send the app for review on Friday; start rolling out the release on Monday. Release team: 1 iOS and 1 Android developers from one of the feature teams; 2 QA engineers. create a new release branch on Wednesday; make a schedule one quarter ahead, indicating when it's each team's turn to release. After a quarter get together and extend the schedule. Schematically, our Release Train looked like this: It Didn't All Go Smoothly After a month, it became clear that although the first experience felt great, It was really hard to do a regress every week and be done by Friday; there was no time for hotfixes, and they sometimes happened. In 2021, our regression test used to take 3–4 days on average. We managed to shorten it to 2–3 days in 2022, but sometimes, it would exceed that time frame. We continued covering regression cases with e2e tests, but we didn’t have 100% coverage yet. We had about 70% and 60% coverage of regression cases on each platform respectively. The takeaway from this is that it will likely be uncomfortable to run a release cycle every week. as long as you have regression tests taking a few days to complete, The Final Answer We ended up moving to 2-week release cycles, and Release Train now looks like this: release every 2 weeks; create a release branch on Wednesday morning; regress, and send the app for review on Friday; start rolling out the release on Monday. Release team: 1 iOS and 1 Android developers from one of the feature teams; 2 QA engineers. make a schedule one quarter ahead, indicating when it's each team's turn to release. After a quarter, get together and extend the schedule. roll the release out gradually; do the hotfixes if needed, now when we have time for them; a week later on Wednesday, create a new release branch. This is how the process looks if everything goes according to plan: It all looks like a weekly cycle except there’s plenty of time left for potential hotfixes. This is what it will look like in case of extended regression tests: No big deal either; there’s still time even for hotfixes. How Did the New Process Affect Predictability? The main goal for us was . It can be broken down into two parts: to increase predictability When will the application be released; In which release will my feature get into? We answered the question “When will there be a release?” by implementing the Release Train process. Now, each team will be able to answer the question “in which release will my feature end up?” independently at the moment when they plan and evaluate the feature. Before, it was impossible to say for sure because another team might or might not do the release. Now, everything depends only on that team’s own planning. To further confirm this, we conducted surveys amongst mobile developers, QA, and product managers, where together, with other questions, we asked: When is the next release? (100% answered this question). Did Release Train help you plan your teamwork? (75% answered positively, but some perfectly predicted their work even without a Release Train). Release Train has also helped us with code freezes and release freezes. We had several of them, in addition to New Year’s Eve (for example, September 1st and some holidays). Now, with Release Train, we don’t have to adjust to these dates by creating release branches, regress testing, and all that. Releases work on schedule; we just open them in the stores later. Impact on Metrics Beyond just solving problems, we also measured metrics. Let’s take a look at the main ones. Lead Time The first important metric we measured was the . Lead Time commitment to release This is what the graph looks like. I marked the point when we started the Release Train process with an arrow. The graph shows that Lead Time went down to somewhere around six days. Is six days a long or short time? Google Benchmarks There are , but it’s mostly for the backend. On their scale, they distinguish the following groups: benchmarks from Google for this metric Elite: less than an hour High: 1 hour to 1 week Medium: 1 week to 6 months Low: 6 months or more I believe that for standard mobile apps, lead time should ideally aim for half the length of the release cycle. This is equivalent to merging a task into the main branch every day. This said, . if the release cycle is 14 days, Lead Time should aim for 7 days Bugs Per Regression Another metric we track is the number of bugs per regression. It describes how stable the release candidate is. If the previous release was a long time ago, then we probably created a lot of new code that might potentially contain a high number of bugs, and we might spend more time on regression and fixes. At one point, this metric was down to three bugs. The specific numbers aren’t that crucial, but overall, you can see that the trend has gone down. More Metrics I’ll briefly discuss what metrics were also monitored as part of the Release Train. Crash-free. We always keep an eye on this metric. There was a hypothesis that it would drop due to our attempts to fit the regression into a tighter timeframe. Well, no drop happened. We wondered if frequent (weekly) releases would have an impact on customer churn and on deleting the app. As a result, we detected no impact. Implement, Improve We like the current process as we think it has achieved its goals. We also know how to improve it further: We continue to work on automating the regression to make it simpler and faster. So far, we have left out the part on work automation (scripts for branching), but this would also be a great point of growth in the future. Our app works in 20 countries, and we need to translate the application into many different languages. There is an internal service for this, but developers still have to participate in this process manually before release. Automating this process potentially might improve the release cycle even further. Summary While we were relatively small, we didn’t need a Release Train. When we faced the fact that we couldn’t predict releases, their size, and number, we decided to implement Release Train. At first, we tried weekly release cycles, but because of time-consuming regressions, we had to switch to two-week release cycles. We’ve been living this way ever since. Now, we have predictability of releases, and metrics show positive dynamics. We plan to increase the coverage of regression cases with e2e-tests, automate the process of working with branches, and optimize the process of translations. I hope this article and our experience will help you, especially if you have already faced similar issues and they made you think about the release process. Thank you so much for reading my article. I hope you enjoyed it. If you have any questions or suggestions, drop me a line in the comments, and I’ll be sure to read it! And . Usually, I post about Android development and software engineering in general. follow me on Twitter Also published here

What Problems Does Release Train Solve in Mobile App Development?

মোবাইল অ্যাপ ডেভেলপমেন্টে রিলিজ ট্রেন কোন সমস্যার সমাধান করে?

Welche Probleme löst Release Train bei der Entwicklung mobiler Apps?

¿Qué problemas resuelve Release Train en el desarrollo de aplicaciones móviles?

Quels problèmes Release Train résout-il dans le développement d’applications mobiles ?

मोबाइल ऐप विकास में रिलीज़ ट्रेन किन समस्याओं का समाधान करती है?

Release Train はモバイルアプリ開発におけるどのような問題を解決しますか?

모바일 앱 개발에서 Release Train은 어떤 문제를 해결합니까?

Que problemas o Release Train resolve no desenvolvimento de aplicativos móveis?

Какие проблемы решает Release Train при разработке мобильных приложений?

Sürüm Treni Mobil Uygulama Geliştirmede Hangi Sorunları Çözer?

Bản phát hành có giải quyết được vấn đề gì trong quá trình phát triển ứng dụng di động?

Release Train解决了移动应用开发中的哪些问题？

260 Stories To Learn About Team Productivity

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Let's learn about via these 79 free stories. They are ordered by most time reading created on HackerNoon. Visit the to find the most read stories about any technology. Trends /Learn Repo 1. 3 Reasons Why Wordle is So Addicting Let's discuss three reasons behind the immense popularity of the Wordle game. 2. Culture Eats Diversity for Breakfast At a group job interview for a San Francisco startup, a woman hiring manager shot a Nerf arrow at my head to demonstrate the cliché “play hard” mentality in an interview setting. At first, I was embarrassed to be singled out in this way. Then, I was appalled. I wondered why any professional would do this, let alone a woman to another woman. The answer: company culture. 3. fitlive is a Better, Easier Way to Provide a Virtual Fitness Experience Fitness instructors are not necessarily all digital natives, although many have large followings. 4. Why is TikTok Obsessed With Tarot Card Readings? First of all, Nostradamus has nothing to do with Tarot. 5. Top Nodejs Trends to Follow in 2021 After AngularJS, Nodejs is another JavaScript technology that has won the hearts of millions of developers around the world. 6. 9 Data Trends You’ll See in 2023 2022 saw the data space grow by leaps and bounds. Here are the top 9 things our team of data experts expects to see in 2023. 7. Blanket Statements Do Not Help Define NFTs Most people (bloggers and journalists included) don’t have the time or the dedication to dive deep into it or really think deeply about blockchain. 8. Top Crypto Trends from 2016 to 2021 [Infographic] We can define recent years as the rise of a technology (DeFi, NFT, etc.) on the crypto market. The thing is that most of them rise long after their invention. 9. Play-to-Earn Games Regulate Ownership and Support NFT Growth Play to Earn games can become a new trend in the video game industry. Find out what Play to Earn games are and how this approach works below. 10. Developer Trends from Thoughtworks’ Technology Radar 27 Thoughtworks’ Technology Radar is a regular time to take a look at what trends are changing our industry. 11. Web Technology Trends in 2022: PWAs, Browsers as OS, Cloud Access Why Google Chrome is the most popular browser nowadays and what web development trends can shape that? 12. Quick Guide to the Python Ord Function In Python, the ord() function accepts a single unit of character and returns the equivalent Unicode of the passed argument 13. Revelation Hackathon Sponsors, Ankr, Celer, and NodeReal Announce BAS Testnet, a BNB Chain Sidechain BSC Application Sidechain (BAS) Testnet is a framework that enables dApps to create sidechains in the BNB Chain ecosystem. 14. NFTs are Not Dying, They are Evolving NFTs are now transforming into a useful instrument that can solve pain points in different industries and generate new opportunities for companies. 15. 7 Tips for Product Managers in Web3 Everything you need as a product manager to enter web3. Here is a list of trends and resources you should focus on to succeed. 16. The British Are to Blame for the Term "Soccer" The British coined the term soccer, NOT Americans 17. 5 Tech-Driven Solutions to Boost Your Business: Meet the 2021 Upgrades Here is a list of some of the best tech upgrades you should consider adding to your business this year. 18. 6 Not-so-obvious Tech Predictions By the time of writing of this article everyone knows, the "technologies of the future" as listed from various mainstream media. And I agree with most of them. It is worth discussing a few not so obvious though. I expect my "predictions" to be fulfilled by 2030. 19. 6 Best Web 3.0 Trends We Will See in 2023 The introduction of deep machine learning capabilities, artificial intelligence (AI), and decentralization are all part of the building blocks... 20. 3 Insurtech Trends We Will See More Of in 2020 In today’s digital world, all industries are making a massive shift towards technology to improve their products and services. Customers expect their problems to be addressed as fast as possible. And, those companies that can achieve that are going to put themselves in the driver’s seat. 21. Healthcare Technology Trends, Digital Innovations in 2022 Explore the main tech innovations that have the potential to transform your healthcare organization in 2022. 22. Gen Z and Millennials are Redefining Mobile Application Development What are the qualities of a good application for Gen Z and Millennials?" 2023 trends for app design and development requires user-friendly interface, security 23. 5 Trends That Show Where eCommerce Is Heading in 2021 Contributing significantly to eCommerce and its spiking sales levels, consumer habits, behaviors, and shopping trends in the USA are changing. And this change is not about to stop anytime soon, with post-Covid trends in eCommerce continuing to place further benefits on this sector and those that take advantage of these trends. 24. Link up With the Best Blockchain Development Companies in 2021 Although blockchain is a relatively young industry, many companies have emerged that offer Blockchain Development Solutions. 25. 10 Trendy Web Design Options to Make a Website That Converts What are the leading web design trends of 2021 are they worth investing in? From retro fonts to Augmented Reality to 3D modeling — there’s a lot to enjoy. 26. Reviewing 2021's Google Shopping Holiday Retail Trends We analyzed Google Ads data for the Holiday shopping season to look at what the trends were for ads, advertised, and sales/discounts in 2021. 27. Can People Reclaim Their Culture and Heritage With NFTs? Technology and cultural heritage can present a somewhat awkward partnership. 28. Science Labs of the Future: Perspectives and Predictions On AI, Automation and the evolving role of the scientist 29. SocialFi — Social Networks on the Blockchain & What to Expect From Web 3 How do social networks of the future differ from the usual ones, and what projects to expect in 2023. 30. An Insider's Look at the Fashion of Hollywood: Going Surreal with Season 2 of Euphoria Hit-show Euphoria is back with season two, with major fashion changes that always keep us on our toes and pull us closer to the characters. 31. How Do I Build High-Volume dApps With Ultra-Low Gas Fees? Like a #BAS BNB Application Sidechains (BAS), a community-driven framework built for BNB Chain, will go live with three decentralized applications. 32. 2023 May Spell Hope For Financial Markets & Crypto Transitioning into the new year, 2023, feels like a much-needed fresh start for cry on many accords. 2022 delivered some of the toughest conditions to remember. 33. Influencer Marketing Predictions for 2022 from Industry Experts What will be trending in 2022? What ideas and concepts will drive the flow? Industry experts are sharing their opinions. 34. The World Population Crosses 8 Billion People A look at the growth trends of the world's 8 billion people and beyond. 35. Investment Trend in Generative AI Shows No Signs of Slowing Down Generative AI refers to the use of machine learning to create new content. It has the potential to revolutionize industries 36. ‘Web3 Mass Adoption is Changing the World for Good,’ says Lakshmi Devan Lakshmi Devan, the Marketing Director of WOW Summit, shares her thoughts on the Web3 marketing trends in 2023. 37. The Future of Email Working in Sales for the past 5 years means the browser tab I most consistently have open is my email. It's also one of the very few active app notifications I have on my phone. 38. Ilya Rouss: “Travel Trends to Watch: digital nomads, non-mainstream places, local economies growth” When the travel industry will recover after the pandemic, what will the travel experience look like and how modern tech will influence it? 39. Stablecoins, the Dollar, and the Top 5 Ideas to Watch Out for in the Second Half of 2022 A way to manage crypto’s trademark volatility, what a concept. Stablecoins are one of the most successful ideas that the cryptocurrency industry has produced. 40. Subscriber Retention: Welcome to the 'Streaming Wars' The streaming space is clearly the future of entertainment. 41. A Look at the Trends in Developer Jobs: A Meta Analysis of Stack Overflow Surveys I'm really interested in the trends we see in the software engineering job market. 42. How to Lose Your Legitimacy Like feudalism and divine right monarchy before it, the creator economy (at least, in its current, highly centralized form) is experiencing a legitimacy crisis. 43. Kids and Tech: What We Know, What We Don't, And What Parents Can Do Today, you'd struggle to find a home that doesn't have multiple smart devices. One side effect is that children are being exposed to technology earlier. 44. Was the Mona Lisa the Result of a Fad? A picture is worth a thousand words. Throughout history, art has gone through many phases like Romanticism, Classicism and many other types of -isms. These are regarded as styles of painting of those periods. However if you think about it, from a modern perspective, these phases can be considered fads, although the more precise term would be trends, due to their longevity. 45. Protect Your Wealth: 10 Financial Predictions for 2022 Almost every government on Earth has started developing its own digital currency. 46. Small Business Digital Marketing Trends To Know in 2023 With so much development happening in the world of digital marketing, what are the trends to look out for in 2023? 47. How Will Blockchain Fix the Centralization of Data? “In order to have a standard of value [cryptocurrency] must stand outside all value schemes. It must have value in and of itself." 48. Technology Is Saving The World Amid COVID-19 Technology - often portrayed as an industry that the benefits only the privileged - is actually helping with the fight against the coronavirus. And we don’t mean Medtech. 49. UX And Agile Trends To Keep You On Top In 2020 The purpose of every product is to be fully functional without forcing users to invest too much time and energy in the process. This is exactly why UX and agile methodologies play such an important role in the 21st century business – everybody wants to design a product that maximizes functionality while minimizing consumers’ efforts. 50. How the Pandemic Hacked the Way We Eat Breakfast [Infographic] A look at how the pandemic shifted American breakfast trends. 51. Welcome to the Age of the Decentralized Storage Systems The decentralized and distributed file storage system, like others, has its strengths and weakness. 52. 6 Ground-Breaking Software Development Trends: 2020 Edition Artificial Intelligence is going to push development 53. Blockchain Trends: The Weak Died and the Strong Evolved 1.1 Three Core Characteristics are Vital for Blockchain Technology 54. The Latest Tech Trends for Boosting Your Business In order for your business to succeed in a market driven by digital devices, you absolutely need to take advantage of the latest developments being made. By staying on top of current trends, you can gain remarkable insight on what consumers want, how to focus your brand identity, and the best methods for increasing sales throughout the fiscal year. Take a look at these ideas that are being utilized by businesses of all sizes and learn how you can prepare your own company for the future. 55. 5 XR Communities to Help Keep up With the Latest XR Trends in 2021 Top 5 XR communities to join and stay updated with the latest XR trends, events, courses, and more in 2021. 56. Can We Predict the Future of Tech? Will the world be a more peaceful place? No it will not. 57. The Clouds are Thickening: An Overview of The SaaS Ecosystem and Big Cloud Providers The global cloud technology market is entering a new stage in its development and is becoming more mature. What to expect next? 58. How to Use Business Intelligence: 66% of Companies Want to Be More Data-Driven in 2021 How do BI solutions help to make the decision-making process driven by data, improve CX, and speed up reporting? And how can you implement it yourself? 59. What Tech Employers Should Know About IT Skills and Trends in 2020 Intro 60. Global Hackathon: 'Revelation' Kicks Off With Up to $10M in Prizes! Hosted By BNB Chain With millions of developers preparing to move to a more decentralized environment, the time has come to pick up the pace and reward early adopters. 61. Impact of Automation on the Future of Work Automation and artificial intelligence (AI) are changing the nature of jobs and many believe that robots will completely put humans out of work in the coming years. 62. Top Tech Trends to Influence Business Recovery in 2022 The year of 2021 came to an end, which means it’s time for us to have a look at all the ups and downs that global business has gone through. 63. 5 Property Management Technology Trends: 2020 Edition Imagine showing the property to your potential tenant through interactive 3D models or Augmented reality models (AR) at a cafe meeting. Wouldn’t it appeal to and save time to your tenant? Imagine your property security personnel patrolling around the property through drones and keeping your property safe from theft or any suspicious activity. Wouldn’t it attract your potential tenant while you are describing it as one of the amenities your industrial building provides? Well, it's time you stop imagining and find a way to implement these technologies in your daily work of property management. 64. Technology Trends of 2021: Our Top Five Picks 2021 is just around the corner, which means it is high time to forecast what the IT industry is holding for us. 65. Top E-commerce Logistics Trends To Follow in 2021 Even though the months passed within the shadow of the COVID-19 pandemic, its effects on the e-commerce logistics industry are still riding a roller-coaster as they scramble to adjust to new limitations, new demands, and ecommerce logistics trends. 66. What is the Long-Term Impact of Living in a Technological World? A technological world sure has its pros and cons 67. Top 5 Gaming Industry Trends for 2022 The gaming industry is rapidly developing, and new trends are constantly emerging. I discuss 5 significant trends in the gaming industry for the rest of 2022. 68. Blockchain in the Outdoor Industry Disruption, disruption, disruption. That has been the name of the game when it comes to new technologies for the better part of the last decade. Virtually every industry has seen this buzzword used in some capacity over the last few years, and rightfully so. New advancements in technology have allowed companies in various industries to attract new customers, provide more value to these customers, and operate more efficiently overall. This has led to a change in the traditional way of doing things as new trends and methods emerge. One industry that is also noticing this trend of disruptive change is the outdoor industry. 69. The Rise of Non-Alcoholic Beverages: Trends to Keep an Eye On Sales of soda drinks in the U.S. have been declining for around a decade while non-alcoholic beverage sales reached $919 billion in just a single year. 70. What lies ahead for crypto-exchanges in 2020? Experts' opinion on the trade war, halving, and Libra The mid-term results for the cryptographic industry in 2019 still remain uncertain. On one hand, market capitalization increased by 125 per cent in the second quarter alone. On the other, users have already lost more than $4 billion this year due to the actions of hackers and scammers, which is more than the entire year of 2018. It is unlikely that 2019 will meet the original expectations. Yet, experts predict a lot of interesting happenings for crypto exchanges in 2020. 71. Angular or React: Which One Should You Choose and Why When to Choose React? When to Choose Angular? Features of React Features of Angular JS History of React JS History of Angular JS React vs. Angular: The Complete Comparison Advantages of React js Advantages of Angular js Disadvantages of React jsDisadvantages of Angular js Which is Better? 72. From Porn To Peloton: 5 Predictions For Tech And Social Media In 2021 This past year has disrupted the tech and social media landscape. What does 2021 have in store? Here are 5 predictions including OnlyFans' new model and more. 73. Top 7 Blockchain Trends for 2020 and Beyond Humanity may be a few steps away from the Internet of Anything and Everything (or whatever the all-encompassing global digital interweb is going to be called in the years to come), but don't kid yourselves: it's coming. Right now the brightest minds in computer science, software development, entrepreneurship, policymaking, and digital arts are hard at work designing the future tenets of the Web 3.0 and the process shows no signs of slowing down. 74. Here's Why Java Is Still Alive And Kicking Let’s see what awaits our favorite Java language directly, and then — what awaits the IT-sphere. 75. TikTok Ban: The Biggest Trends to Arrive from 2020's Controversial Social Network TikTok’s rise in popularity has taken the world by storm. Launched in 2017, the social network only reached world markets in the summer of 2018. Two years on and the video-based platform has amassed over two billion downloads globally. 76. 5 Macro-Trends That Will Impact Businesses Over the Next 10 Years The Big Picture: Macro-Trends That Will Shape the Next 10 Years... And how to take advantage of them 77. The Evolution of The Web as We Know It Website layouts change all the time, and there are more ways than ever to style a layout, with many frameworks available to help simplify the code, whilst keeping sites looking fancy. 78. Taking a Glimpse at The Post-Covid World Like most people, over the past 2–3 months I have spent an unhealthy amount of time incessantly checking for Coronavirus updates, reading opinion pieces and going down Twitter rabbit holes trying to make sense of the new world we now live in. As things started to settle into the new normal of social distancing and self-isolation, I shifted from thinking about the immediate to thinking about the future and was trying to answer one question. What are the the new trends and lasting tailwinds that will shape our future in the wake of this pandemic? 79. Revelation Hackathon Bounties Announced: Hosted by BNB Chain The BNB Chain Revelation Hackathon is a 50-day event co-hosted by DoraHacks. Thank you for checking out the 79 most read stories about Trends on HackerNoon. to find the most read stories about any technology. Visit the /Learn Repo

79 Stories To Learn About Trends

The blog provides an in-depth analysis of Data Mesh architecture, addressing its design, benefits, and implementation challenges. It emphasizes , enhancing operational efficiency and accessibility. The guide serves as an invaluable resource for enterprises aiming to leverage modern data strategies. decentralized data ownership In today's data-driven world, organizations are constantly seeking innovative ways to manage and leverage their vast amounts of data. One emerging approach that has gained significant attention is data mesh architecture. But what exactly is ? Data mesh is a decentralized approach to data architecture that aims to empower individual teams within an organization to own and manage their data domains. Unlike traditional centralized data architectures, data mesh distributes data ownership and governance across various teams, enabling greater scalability, flexibility, and autonomy. data mesh ‍ What is Data Mesh? Data mesh is a paradigm shift in how data is organized and managed within an organization. In a traditional architecture, a centralized data team is responsible for collecting, processing, and storing data. This can lead to bottlenecks, lack of agility, and limited scalability. Data mesh, on the other hand, distributes data ownership and governance to individual teams or domains. Each team becomes responsible for its own data products, including data quality, data infrastructure, and data governance. This decentralization allows for greater agility, scalability, and faster decision-making. ‍ Key Principles of Data Mesh Architecture Data mesh architecture is built on a set of key principles that guide its implementation. These principles include: : In data mesh architecture, teams are organized around specific business domains or areas of expertise. Each team is responsible for managing its own data products and has the autonomy to make decisions regarding data quality, infrastructure, and governance within its domain. Domain-oriented decentralized teams : Rather than relying on a centralized data governance team, data mesh architecture promotes a federated approach to data governance. Each domain team has the authority and responsibility to define and enforce governance policies within their domain, ensuring data quality and compliance. Federated data governance : Data mesh architecture encourages the provision of self-serve data infrastructure to empower domain teams. This includes tools and platforms that enable teams to easily access, process, and analyze data within their domain, reducing dependencies on centralized data engineering teams. Self-serve data infrastructure : Data mesh treats data as a product and promotes product thinking within domain teams. This means focusing on delivering high-quality data products that meet the specific needs of consumers while continuously iterating and improving based on feedback. Product thinking ‍ Benefits of Implementing Data Mesh Architecture Implementing data mesh architecture offers several benefits for organizations: : By distributing data ownership and governance, data mesh architecture enables organizations to scale their data capabilities without creating bottlenecks. Each domain team can independently manage and scale its data products, allowing for greater agility and scalability. Scalability : Data mesh architecture provides flexibility by allowing teams to choose the most appropriate technologies and tools for their specific needs. This avoids a "one-size-fits-all" approach and empowers teams to experiment and innovate with new technologies. Flexibility : Data mesh architecture grants autonomy to domain teams, empowering them to make decisions regarding data quality, infrastructure, and governance within their domain. This reduces dependencies on centralized teams and enables faster decision-making. Autonomy : With domain teams taking ownership of data quality, data mesh architecture promotes a culture of accountability and responsibility. Each team is responsible for ensuring the quality of their data products, leading to improved data accuracy and reliability. Improved data quality ‍ Common Misconceptions about Data Mesh Architecture While data mesh offers several benefits, there are also common misconceptions that need to be addressed: : Some argue that data mesh architecture introduces additional complexity by distributing data ownership and governance across multiple teams. However, proponents of data mesh argue that this complexity is outweighed by the benefits of scalability, flexibility, and autonomy. Increased complexity : Critics argue that data mesh architecture lacks centralized control, leading to potential inconsistencies and data silos. However, data mesh architecture promotes a federated approach to data governance, where each domain team has the authority and responsibility to define and enforce governance policies. This ensures consistency and compliance across the organization. Lack of centralized control : Implementing data mesh architecture may require additional resources to support the infrastructure and tools needed by domain teams. However, proponents argue that the benefits of scalability, flexibility, and improved data quality outweigh the resource requirements. Higher resource requirements ‍ Understanding the Components of Data Mesh Architecture Data mesh also consists of several components that work together to enable decentralized data management. These components include: : Domain teams are responsible for within their specific business domains. They have autonomy over data quality, infrastructure, and governance within their domain. Domain teams managing their own data products : Data products are the outputs of domain teams and can include datasets, APIs, dashboards, or any other form of data that is consumed by other teams or stakeholders. Data products : Data infrastructure refers to the tools, platforms, and technologies that enable domain teams to access, process, and analyze data within their domain. This includes data storage systems, data processing frameworks, and data visualization tools. Data infrastructure : Data governance in data mesh architecture is a federated approach where each domain team has the authority and responsibility to define and enforce governance policies within their domain. This ensures data quality, compliance, and consistency. Data governance Implementing Data Mesh Architecture in Your Organization Implementing data mesh architecture in your organization requires careful planning and consideration. Here are some steps to consider: : Evaluate your organization's current data architecture, culture, and readiness for a decentralized approach. Identify potential challenges and areas that need improvement. Assess your organization's readiness : Determine the domains or areas of expertise within your organization and establish domain teams. Assign clear ownership and responsibilities to each team. Define domain boundaries : Provide domain teams with the necessary tools, platforms, and technologies to enable self-serve data infrastructure. This includes data storage systems, data processing frameworks, and data visualization tools. Enable self-serve data infrastructure : Define governance policies and guidelines that each domain team should follow. Encourage collaboration and communication between teams to ensure consistency and compliance. Establish federated data governance : Foster a culture of accountability and responsibility within domain teams. Encourage teams to take ownership of their data products and continuously improve data quality. Promote a culture of data ownership ‍ Challenges and Considerations for Adopting Data Mesh Architecture While data mesh architecture offers numerous benefits, it also presents challenges that organizations need to consider: : Implementing data mesh architecture requires a significant cultural shift within the organization. It may be challenging to change existing mindsets, processes, and workflows. Change management : Distributing and governance across multiple teams can introduce technical complexity. Organizations need to ensure that domain teams have the necessary skills and resources to manage their data products effectively. Technical complexity data ownership : Data mesh architecture can potentially lead to data inconsistencies and integration challenges. Organizations need to establish clear guidelines and communication channels to ensure data consistency and facilitate data integration. Data consistency and integration : Data mesh architecture requires strong alignment and collaboration between domain teams. Organizations need to invest in fostering a culture of collaboration and communication to ensure the success of data mesh implementation. Organizational alignment ‍ Case Studies of Successful Data Mesh Implementations Several organizations have successfully implemented data mesh architecture. Here are two notable case studies: , an giant, adopted data mesh architecture to overcome scalability challenges. By distributing data ownership and governance to domain teams, they achieved faster decision-making, improved data quality, and increased scalability. Floward e-commerce , a financial services organization, implemented data mesh architecture to enable faster product development. By empowering domain teams with self-serve data infrastructure, they reduced dependencies on centralized teams and achieved greater agility. PayU ‍ Resources for Learning More about Data Mesh Architecture If you're interested in learning more about data mesh architecture, here are some recommended resources: : " " by Zhamak Dehghani provides a comprehensive guide to implementing data mesh architecture. Book Data Mesh: Delivering Data-Driven Products with Team Data : The ThoughtWorks Insights blog features several articles on data mesh architecture, including case studies, best practices, and implementation tips. Blog : The Data Mesh Podcast hosted by Zhamak Dehghani explores various aspects of data mesh architecture through interviews with experts and practitioners. Podcast ‍ Conclusion Data mesh architecture offers a decentralized approach to data management, empowering individual teams within an organization to own and manage their data domains. By distributing data ownership and governance, organizations can achieve greater scalability, flexibility, and autonomy. While implementing data mesh architecture presents challenges, the benefits of improved data quality, faster decision-making, and increased scalability make it a compelling approach for organizations seeking to leverage their data effectively. By understanding the key principles, components, and considerations of data mesh architecture, organizations can embark on a successful journey toward a more decentralized and agile data architecture. About me: Jatin is the founder of - A unified platform to deliver data reliability. Decube

A Comprehensive Guide to Understanding Data Mesh Architecture

Let's learn about via these 51 free stories. They are ordered by most time reading created on HackerNoon. Visit the to find the most read stories about any technology. Terraform /Learn Repo 1. 6 Free Terraform Tools to Facilitate a Multi-Cloud Infrastructure Have a multi-cloud infrastructure? These free Terraform tools will help you to reduce costs and make your multi-cloud efficient. 2. Developer Trends from Thoughtworks’ Technology Radar 27 Thoughtworks’ Technology Radar is a regular time to take a look at what trends are changing our industry. 3. Serverless API with Terraform: GO and AWS [Part 2] In part 2, we'll go over deploying serverless API to AWS Cloud with Terraform. 4. How a Unicorn Startup in Japan Leveraged the Power of Microservices Startup experience in Mercari. Road to microservices from monolith. 5. Getting Started With Creating, Managing an AWS ECS Cluster With Terraform In this tutorial, you'll define AWS resources using Terraform and how resource definitions translate to actual resources created on AWS 6. Basics of AWS Tags & Terraform with S3 - Part 1 In this video course, you'll learn the basics of AWS tags and Terraform with an S3 bucket. 7. How to Test Terraform the Right Way In this blog post, you'll learn about why you should be testing IaC and the best platforms to get started with. 8. Static Code Analysis  for Infrastructure as Code Using Azure DevOps Pipelines This article explains how to set up static code analysis for infrastructure as code using Azure DevOps pipelines. 9. Should Deploying Production Infrastructure be Hard? Below I'll explain why I built terra_boi to deploy Clientelify, why I think it was downloaded 1,000 times in the first 10 days (despite not promoting it anywhere), and why deploying an application to production shouldn't suck. 10. How To Manage Infrastructure With Terraform What is Terraform? 11. Getting Started Provisioning an AWS EKS Kubernetes Cluster with Terraform Learn how to provision an AWS EKS Kubernetes cluster with Terraform. See a full step-by-step tutorial. 12. How Infrastructure as Code Is Forcing a Revolution in Cost Management You might have read our beta launch blog post, which describes how Infrastructure as Code (IaC) is driving the third datacenter revolution (the first two being virtualization and the move to public clouds). In this post, I want to go deeper into looking at how IaC is driving a new need and approach to cost visibility and management. 13. Automating User Management in the Company Have you ever faced the challenge of automating users in your company? 14. Introducing Driftctl: Your IaC Security Belt We recently released the first versions of driftctl, a new open-source project for infrastructure developers, DevOps, SRE, and cloud practitioners, with the goal of helping manage all kinds of drifts. 15. Deploying a Terraform Remote State Backend with AWS S3 and DynamoDB In this article, we are going to be deep diving into remote state management in terraform. 16. Another Reason to Use Docker Recently I was working on a project which includes Terraform and AWS stuff. While working on that I was using my local machine for terraform code testing and luckily everything was going fine. But when we actually want to test it for the production environment we got some issues there. Then, as usual, we started to dig into the issue and finally, we got the issue which was quite a silly one 😜. The production server Terraform version and my local development server Terraform version was not the same. 17. Building Maintenance Mode for your API Gateway Using Terraform In my previous blog post, I went through how to create a maintenance mode page for your application, and how to implement it using Terraform and github pages. But the website is just one part of an Application, and often there’s also a public (or private) API that also needs to have a maintenance mode. Let’s see how we can do that using Terraform on API Gateway. 18. How to Deploy a Django Application on AWS with Terraform The final goal of this guide is to create a scalable and reproducible setup of the Django Web Application on AWS using Terraform. 19. How To Sync Terraform Config And .tfstate File For Existing AWS Resources Terraform is a great (and dominant) infrastructure automation tool. It is multi-cloud, can build all sorts of resources, and in some cases supports API calls to build resources before the native tooling from cloud providers does. 20. Managing Active Directory Objects with Azure AD Provider for Terraform Learn how to manage Active Directory Objects with Azure AD Provider for Terraform. See examples of how to authenticate and grant the correct permissions. 21. Why you should take care of Infrastructure Drift When talking about infrastructure drift, you often get knowing glances and heated answers. Recording gaps in your infra between what you expected to be and the reality of what is, is a well known and widespread issue bothering hundreds of DevOps teams around the globe. 22. Managing a GitHub Organization With Infrastructure as Code This post will share our experience in adopting infrastructure as code (IaC) to manage GitHub organization resources. 23. Using Jupyter/TensorBoard In Any Cloud With One Command Using Terraform Provider Iterative for bespoke Machine Learning & Data Science on AWS, Azure, GCP and K8s 24. The Top 5 DevOps Tools and Services to Consider as a Startup A collection of DevOps tools can significantly enhance your Software Development Life Cycle (SDLC) and increase engineering productivity. 25. Change the Name of your AWS S3 Bucket in Terraform Without Breaking Things Learn how to rename an AWS S3 bucket in Terraform. See renaming process of Amazon s3 bucket in a few simple steps. 26. Exporting Your GKE Cluster to Terraform Cloud: A Guide with Challenges and Solutions In this article, I'll share our journey at ANNA Money from using our Kubernetes installation in GCP to the managed Kubernetes service by Google and GKE. 27. Hashicorp's Terraform: A Introduction Join us in the journey of learning Terraform. This blog series aims to provides a guidance for essentials to be known while working with Terraform. 28. A Handy Guide to Migrating Terraform State to GitLab CI/CD Migrating Terraform state to GitLab CI/CD is a GitOps best practice that allows multiple engineers to work together to develop infrastructure. 29. How Namespace and Terraform Can Manage Kubernetes Environments In the last several years, Kubernetes has become the “go to” standard for managing and orchestrating containerized workloads. Thanks to it’s vendor agnostic nature, you can easily run Kubernetes almost anywhere, and in fact, all the major cloud vendors offer a managed Kubernetes service (AWS EKS, Google GKE, and Azure AKS). 30. Building a “Maintenance Mode” with Terraform and Github Pages As you may have seen, we just launched our public beta of env0 last month. As part of the run-up to the launch, our dev team had to go through and make sure everything about our infrastructure was ready for ongoing public use: one element being creating a maintenance mode for both our Application and our public API. 31. How To Migrate An Existing Infrastructure into Terraform Terraform is a powerful tool to have in your toolset. 32. How To Setup Continuous Integration Pipeline By Using Terraform And GitLab CI Terraform is a fantastic tool for managing your cloud infrastructure, especially if your assets are hosted on multiple cloud providers. 33. Learn How to Build a CI/CD Pipeline Using GitLab for your Website We'll run through how to build and configure a CI/CD pipeline to deploy your business's website using AWS, Terraform & Gitlab. 34. How to Optimize for Iteration: Choosing Your Early Startup's Tech Stack As we’ve discussed in a previous blog post, product development is hypothesis testing. This is especially true in the early stages of a company when you need to confirm or reject your hypothesis as quickly as possible. This process is then repeated until you (hopefully) reach product-market fit. To get there, your team needs to be able to work and build at a pace that allows for this constant and rapid iteration. 35. What Really Propelled The Cloud Revolution: A Developer's Perspective An overview of the main trends that have pushed the Cloud industry — and the world — forward, in the past decade and a half. 36. Sanity-Checks with Playwright in Fargate container on schedule There is often the need to check whether your production system is working as expected. Playwright is a great way to test if everything is going to plan. 37. From “Feature branches” to “Feature environments” with Terraform “The advantage of feature branching is that each developer can work on their own feature and be isolated from changes going on elsewhere.” (FeatureBranch) 38. Serverless API with Terraform, GO and AWS, Part 1 Knowing how to build REST API with latest tech is cool. You know what's even cooler? It's being able to deploy it to the cloud! I'll walk you through the process of building simple, server-less application using GO, AWS (Lambda, API Gateway, Dynamodb) and Terraform. 39. Improve Your IaC With a Specialized Management Platform Most often, there is no point in implementing an in-house solution as the cost and effort of building and maintaining it may exceed its potential benefits. 40. Kubernetes Terraforming on Linode: Linode + Terraform + Kubernetes = Cloud Nirvana Let's discuss the process of "Terraforming" a Kubernetes cluster on Linode using Terraform to help organizations efficiently manage and scale  infrastructure. 41. 10+ Best Resources to learn DevOps in Depth [Online Courses] DevOps is one of the most in-demand skills from employer and there are many job opportunities lying for full stack developers, distinguished engineers and DevOps professionals. If you are an experienced Java programmer or a full-stack web developer, and want to become a DevOps engineer then you have come to the right place. 42. Ad Hoc Environments for Django Applications With ECS, Terraform and GitHub Actions This article will show how software development teams can build on-demand environments for dog-food testing, quality review, internal and external demos and oth 43. Create Your New Reality With Wirehole And Other Awesome Automation Tools **UPDATE October 13, 2020 - The mjtechguy/terransible-wirehole Github repo now supports deploying to Digital Ocean and Hetzner Cloud as well! 44. GraphQL Is Not a Silver Bullet GraphQL is not a configuration language, also not a transformation language, a replacement for SQL, and definitely not Terraform. 45. Building an AWS EKS Fargate Cluster with Terraform How to build an AWS EKS Fargate cluster using terraform. 46. Terraform Configuration Syntax Overview All you need to know to get started about Terraform Syntax. 47. Is Terraform Still Useful? Terraform is an agentless infrastructure tool used mainly by DevOps for infrastructure management. Find out why you should use Terraform for infrastructure. 48. 6 DevOps Trends in 2022 That DevOps Engineers Should Adopt The role of DevOps engineers, their responsibilities, growth opportunities, a set of important soft & hard skills, and most importantly, DevOps trends in 2022. 49. Deploying an Open Source Secure Video Conference Server [A How To Guide] In this article, you'll create a new DigitalOcean account using a free credit link. Then, you will clone a GitHub repository, and use Terraform code to initialize, plan and apply resources to your account, using a preconfigured image from DigitalOcean and your custom domain name. Finally, you will access your account remotely via SSH and execute two scripts to configure both your video conference server and Let's Encrypt SSL to enable HTTPS. 50. Newsflash - "People with AWS Console Access Tend to Change Things" This blog post is a written transcript of the FOSDEM Talk: “Infrastructure drifts aren’t like Pokemon, you can’t catch ’em all”, by Stephane Jourdan – CTO and founder 51. Introduction to AWS Log Insights as CloudWatch Metrics A step-by-step description of how to create an AWS Lambda to convert Cloudwatch LogInsights into metrics Thank you for checking out the 51 most read stories about Terraform on HackerNoon. to find the most read stories about any technology. Visit the /Learn Repo

51 Stories To Learn About Terraform

Let's learn about via these 74 free stories. They are ordered by most time reading created on HackerNoon. Visit the to find the most read stories about any technology. Observability /Learn Repo 1. So, How are Observability and Monitoring Different, Actually? The similarities and differences between monitoring and observability and how to pair the two strategies 2. Observability Tips and Tricks For Using Grafana and Prometheus Rapid prototyping tips when using Grafana and Prometheus observability and monitoring stack. 3. Observability and Monitoring in a nutshell A guide to understanding the concepts and differences between monitoring and observability. 4. Debugging AWS Lambda Runtime Errors Debugging AWS Lambda can get tricky in Amazon CloudWatch. KloudMate lets you get to Lambda errors quickly and effectively using Lambda error logs. 5. An Intro to Dynamic CDK Dashboards How to create a construct based on other constructs in the same stack without forcing the developer to call a method for each? That's exactly what aspects do! 6. N/A please delete this 7. Developer Trends from Thoughtworks’ Technology Radar 27 Thoughtworks’ Technology Radar is a regular time to take a look at what trends are changing our industry. 8. How Observability and Monitoring Produces Better Software An article focused on deep diving into observability and its significance in software. Its history, goals, the importance of observability, and the issues that 9. How to Build Your Own Observability Platform on Kubernettes End-to-end full observability using Odigos and other back-end observability tools. 10. Maintaining Quality When Transitioning from Monolith to Microservices Piece by piece, legacy monolith applications are being broken down and replaced by microservices. 11. How to Monitor Apache Flink with OpenTelemetry Apache Flink monitoring support is now available in the open source OpenTelemetry collector. 12. Observability vs Monitoring: What's the Difference? Monitoring has been a basic system to track the health of servers for years. Now it is not enough. 13. Observability and Monitoring Have a Symbiotic Relationship, but They Are Different Observability vs. monitoring, what is the difference? Monitoring is the what to observability’s why. Here we dig into the differences. 14. Microservice.add(observability) != Microservice.add(monitoring) You are reading this content, which means that you are not novice to the microservices field. So let me just  scratch the surface of it before moving to Observable Microservices.  Once upon a time Monolith Application was now transformed into Microservices based application. 15. How Do You Make Cloud Migration Easier? Engage in intelligent change. 16. Application Monitoring: Closing Observability Gaps with Custom Metrics Which application metrics should you collect for your microservices? 17. The Challenges of Modern Observability It’s tough, impossible, to get a holistic view of your entire application when it’s running on 200 pods deployed to 12 nodes distributed across the world. 18. Why OpenTelemetry Should Matter to Network and Systems Admins OpenTelemetry is young, even by internet standards. Born out of the merger of OpenTracing and OpenCensus projects at the Cloud Native Computing Foundation CNCF 19. What is the ideal memory size to lower costs of running a task on Lambda? Should you increase Lambda memory? It might sound crazy, but increasing your AWS Lambda memory could actually lower your bills. Find out how. 20. What "Shifting Left" in Software Really Means for Blameless DevOps A truly "blameless culture" in software must to evolve from incident reporting, to telemetry aimed at Proactive Observability in DevOps 21. CI/CD/CF — The "Missing-Link" in the DevOps Toolchain? Everyone is familiar with CI and CD processes, but whatever happened to Continuous Feedback? Leveraging observability in dev creates a new type of dev process 22. Why Your Monitoring Dashboard May Be Feeding You Phantom Metrics We trust our metrics to show us the status of our system and where it misbehaves. But do our metrics show us what really happened? 23. Cloud Native Applications: 4 Building Blocks You Need in Your Platform To create amazing cloud native applications your platform needs these components: Kubernetes, Continuous Delivery Pipelines, Edge Stack, and Observability. This article will explain the importance of each component. 24. Comparing Different Serverless Monitoring Platforms Technology touches almost every corner of the world economy. Even when it’s an indirect relation, in many cases tech is an essential, vital part of our societies. It just can’t fail without causing too much distress and losses. Not only financially, but especially to the human aspect. 25. Get Started With Sidekick Open Source Live Debugger in 5 Mins Sidekick is a live application debugger that lets you troubleshoot your applications while they keep on running. Here is how you can start using it in 5 minutes 26. Logging in Observability - Part 1 rsyslog vs journald vs filebeat 27. Managing Collectors on K8 – Why We Chose the OpenTelemetry Collector for Odigos Without proper implementation and continuous configuration of your collectors, observability tools will be limited at best at best and many times ineffectual. 28. On Dynamic Observability and Team Culture with Liran Haimovitch, Rookout CTO Rookout Co-Founder and CTO, Liran Haimovitch, shares the origin story of their debugging tool, what excites him about the startup life, PLG, and more. 29. 9 Data Trends You’ll See in 2023 2022 saw the data space grow by leaps and bounds. Here are the top 9 things our team of data experts expects to see in 2023. 30. Observability and AI Partner Well to Tackle All of That Data Gone are the days when operations and development were a back-office affair. 31. Logging in Observability - Part 2 In this article, I want to describe how to work with logs, analyze them in the command line, and we will consider new modern tools to visualize logs. 32. Tracing: An Essential Component of Observability In this article, I am going to shed light on one important aspect of observability - tracing. 33. Microservice Observability Patterns [Part 1] Logging is one of the most important parts of software systems. Whether you have just started working on a new piece of software, or your system is running in a large scale production environment, you’ll always find yourself seeking help from log files. Logs are the first thing people look for when something goes wrong, or something doesn’t work as expected. 34. What is Observability? Is it Cultural? In order to leverage observability we need a significant shift in our corporate culture that encapsulates the entire company & goes beyond the tools. 35. Murre - the lightweight K8s metrics monitoring tool 36. Introductory Guide to Debugging Lambda Configuration Errors The “Lambda configuration error” is as generic as it gets but at the end of the day, it's a pathing issue. Let's go over how to resolve this. 37. Contextual Intelligence and Observability: Without the Former, You Really Don’t Have the Latter Observability is a hot term in the industry, but don’t let it fool you: having visibility into your organization's apps and services only gives you a piece. 38. The Everything Guide to Data Collection in DevSecOps Collecting wide swaths of observability and security data is key to a high-quality digital experience. Find out what you need to know to get started. 39. Better Performance and Security by Monitoring Logs, Metrics, and More Monitoring is a crucial part of observability. Learn how monitoring can specifically improve security, performance, and reliability. 40. Goodbye, Cost-depth Trade Off: Cloud-Native Observability, Redefined It turns out that, by rethinking the fundamentals of your approach to monitoring, you can have it all 41. Prometheus — Easy In, Difficult Out Setting up a Prometheus server can be easy, but getting actionable information out is a very long and never-ending journey. 42. Choosing a Computing Method: a Serverless SWOT Analysis If you ever find yourself deciding for or against serverless the following tries to make the decision easier for you. 43. Setting Up AWS CloudWatch Alerts (vs Dashbird Alerts) To Monitor Your Applications Learn about the best practices for AWS Cloudwatch Alerts and Dashbird Alarms, to not miss out on critical info about your serverless app. 44. Exploring Differences Between Monitoring And Observability Monitoring vs Observability: in this article, we're explaining what is observability exactly and how does it differ from monitoring. 45. Kuma and Prometheus for Observability in Kubernetes Microservices Clusters A year ago, Harry Bagdi wrote an amazingly helpful blog post (link at bottom of article) on observability for microservices. And by comparing titles, it becomes obvious that my blog post draws inspiration from his work. 46. To Log Or Not To Log, Is The (Eternal) Question An alternative logging strategy to make loggers your friends, not enemies 47. 2023 Observability Predictions: Open Source is the Key to Explosive Growth The observability industry is projected to grow about 8% in 2023, a continuation of a strong growth trend since the 90’s. 48. How Goji Investments Enhances Developer Experience via Observability This post was written by Dean Record, Engineer at Goji Investments. 49. What is API Observability? APIs are the blood vessels of digital business. 50. A Guide to Deploying Jaeger on Kubernetes in Production Logs, metrics, and traces are the three pillars of the Observability world. The distributed tracing world, in particular, has seen a lot of innovation in recent months, with OpenTelemetry standardization and with Jaeger open source project graduating from the CNCF incubation. 51. How To Use a FastAPI Abstraction To Query AWS Data Lake Our guide in creating FastAPI abstraction that allows us to query the AWS Glue metadata catalog - a Data Discovery API for AWS Data Lake. 52. Sizing Your Cloud Resources: 10 Common Mistakes to Avoid Learn the common pitfalls of moving to the cloud and how you can avoid them to truly benefit from the cloud’s elasticity. 53. CNCF Tools Overview: Are You Cloud-Native? Cloud computing is becoming more and more of a household name, with even the most conservative fields of business figuring out how to make the best use of it. Cloud computing usually starts with running a private cloud solution on premises before venturing onto the public cloud. Of course, the cloud is not a single uniform being. It may come from different providers, Amazon Web Services, Google Cloud Platform, and Microsoft Azure being the biggest players here. Or it may come with different visibility and hosting, that is, public (resides with the provider), private (self-hosted), or hybrid (which uses a bit of both). And the cloud can use different tools and APIs for management as well. 54. Microservice Observability Patterns [Part 2] In my previous article, I talked about the importance of logs and the differences between structured and unstructured logging. Logs are easy to integrate into your application and provide the ability to represent any type of data in the form of strings. 55. Improving Observability Through Service Level Metrics 56. Observability on Heroku: How to Monitor Apps on a Managed Infrastructure Cloud platforms like Heroku make it easier than ever to host applications: just upload your code, and they’ll deploy it for you. But a common misconception is that, because you don’t own the infrastructure, you can’t really monitor your applications or see under the hood. 57. Setting Up Notification Systems to Observe and Analyze Your Application By building observability and analytics into your notification system, you can identify and quickly resolve issues by monitoring how your product is performing. 58. Better Failure Detection in Serverless Apps Having reliable failure detection in place is a must for any application in production. Here's how to best achieve it for AWS Lambda. 59. 4 Key Observability Metrics for Distributed Applications A common architectural design pattern these days is to break up an application monolith into smaller microservices. Each microservice is then responsible for a specific aspect or feature of your app. For example, one microservice might be responsible for serving external API requests, while another might handle data fetching for your frontend. 60. Lua-Land CPU Flame Graphs in OpenResty XRay This post will introduce the idea of Lua-land CPU flame graphs and use OpenResty XRay to produce real flame graphs for several small and standalone Lua examples 61. 5 Key Takeaways from New Relic’s 2022 State of Logs Report The 2022 State of Logs Report provides a detailed view of how this practice is shaping engineering and the technologies of the future. 62. Introduction to Observability in ITOM and AIOps Observability is a best practice implemented by AIOps, enabling automation and expanding visibility into the entire organizational ecosystem. 63. Internal AWS Monitoring is Hard to Grasp Learm how a serverless monitoring solution can catch problems for you without the painful learning curve connected to serverless failure detection. 64. Jaeger Persistent Storage with Elasticsearch, Cassandra and Kafka Running systems in production involve requirements for high availability, resilience and recovery from failure. When running cloud-native applications this becomes even more critical, as the base assumption in such environments is that compute nodes will suffer outages, Kubernetes nodes will go down and microservices instances are likely to fail, yet the service is expected to remain up and running. 65. How to Monitor Serverless Applications With AWS CloudWatch Alarms Running any application in production assumes reliable monitoring to be in place and serverless applications are no exception. 66. How to Optimize Large Scale Serverless Applications for Operational Excellence Managing applications at scale often comes up as one of the biggest concerns for businesses; How can it work smoothly? How do we monitor so many resources? How do we maintain best practices with constantly evolving infrastructure? In this article, we run through the best approach for operational excellence looking at serverless monitoring strategy, serverless alerting strategy, and security and compliance best practices. 67. Fixing The ClickHouse Node Failure On Distributed Systems - A How-To Guide Part One: ClickHouse Failures, by Marcel Birkner 68. Introduction To Distributed Tracing Pattern A distributed architecture brings in several challenges when it comes to operability and monitoring. Here, one may be dealing with tens if not hundreds of microservices, each of which may or may not have been built by the same team. 69. 10 Threats to an Open API Ecosystem Despite tight economic situations worldwide, the API economy continues to grow. 70. Data Access for Microservices If you want to access data in a distributed environment such as in a microservice architecture, then data services are the way to go. The idea is to create a data abstraction layer (DAL) that the rest of the system’s applications and services can share. Thus, a data service gives you a generalized interface to the data you’re exposing and provides access to it in a standard manner. This would be in a well-understood protocol and a known data format. For example, a popular approach is to use JSON via HTTP/S. 71. Rethinking Programming: Automated Observability Introduction 72. Watching the Requests Go By: Reconstructing an API Spec with APIClarity In this post, we’re going to look at what API reconstruction is and how APIClarity solves the API observability problem. 73. Navigate Your Way to Production Bliss with Caretta The easiest way to map our cluster: Caretta - a standalone OSS tool, leveraging eBPF technology to be lightweight and frictionless 74. The Importance of Monitoring Big Data Analytics Pipelines In this article, we first explain the requirements for monitoring your big data analytics pipeline and then we go into the key aspects that you need to consider to build a system that provides holistic observability. Thank you for checking out the 74 most read stories about Observability on HackerNoon. to find the most read stories about any technology. Visit the /Learn Repo

74 Stories To Learn About Observability

86 Stories To Learn About C++

140 Stories To Learn About Agile Software Development

Automated tests can be run on multiple environments, multiple browsers, multiple devices and multiple data sets in parallel. These tests are less prone to human error than manual tests, which can help improve the accuracy and reliability of the testing process. Furthermore, they are repeatable, consistent and reliable which helps in reducing false positives and false negatives. In this article, I’ll talk about why you should be using automated tests in Selenium, JUnit5 and Gauge Framework. Increases Efficiency Automation tests can be run faster than manual tests, allowing more tests to be run in a shorter time period. This can help identify issues and bugs more quickly, saving time and resources in the long run. Automation tests can run on multiple environments, multiple browsers, multiple devices and multiple data sets in parallel which increases the overall efficiency of the testing process. import com.thoughtworks.gauge.Step
import org.openqa.selenium.By
import org.openqa.selenium.WebDriver
import org.openqa.selenium.chrome.ChromeDriver

class AutomationTest {
private lateinit var driver: WebDriver
@Step("Open the browser and navigate to the website")
fun openBrowser() {
    System.setProperty("webdriver.chrome.driver", "/path/to/chrome/driver")
    driver = ChromeDriver()
    driver.get("https://www.example.com")
}

@Step("Search for <searchTerm>")
fun search(searchTerm: String) {
    driver.findElement(By.name("q")).sendKeys(searchTerm)
    driver.findElement(By.name("btnK")).click()
}

@Step("Close the browser")
fun closeBrowser() {
    driver.quit()
  }
} This is an example of a simple test automation script written in which is using the library to interact with a web page, and the to organize and run the tests. This script opens a browser, navigates to a website, performs a search, and then closes the browser. Kotlin Selenium Gauge framework Because this test is automated, it can run faster than if it were performed manually. This is just a simple example, in a real-world use-case, you can have multiple scenarios, multiple test cases, and multiple test suites which can be automated and run in parallel, increasing the overall efficiency of the testing process. It's worth noting that, if you are working with a web application, there are other frameworks and tools that can be used in conjunction with or instead of Selenium, such as Cypress, WebDriverIO, and TestCafe, each one having its own specific features and advantages. Improves Accuracy Automated tests are less prone to human error than manual tests, which can help improve the accuracy and reliability of the testing process. Automated tests are repeatable, consistent and reliable which helps in reducing false positives and false negatives. import com.thoughtworks.gauge.Step
import org.openqa.selenium.By
import org.openqa.selenium.WebDriver
import org.openqa.selenium.chrome.ChromeDriver

class AutomationTest {
private lateinit var driver: WebDriver

@Step("Open the browser and navigate to the website")
fun openBrowser() {
    System.setProperty("webdriver.chrome.driver", "/path/to/chrome/driver")
    driver = ChromeDriver()
    driver.get("https://www.example.com")
}

@Step("Search for <searchTerm> and verify the result")
fun search(searchTerm: String) {
    driver.findElement(By.name("q")).sendKeys(searchTerm)
    driver.findElement(By.name("btnK")).click()
    val searchResult = driver.findElement(By.xpath("//div[@class='g']"))
    assert(searchResult.text.contains(searchTerm))
}

@Step("Close the browser")
fun closeBrowser() {
    driver.quit()
  }
} In this example, the script not only performs a search, but also verifies the results by asserting that the returned search results contain the search term. This helps ensure the test is accurate and will catch any issues that may occur with the search functionality. Because the script is automated, it will run the same way every time it is executed with the same inputs, ensuring the test is repeatable, consistent and reliable. This helps in reducing false positives and false negatives, which can occur with manual testing. Additionally, you can use test reporting tools like Gauge report, Allure, TestNG, JUnit, etc. to track test results, allowing you to see if a test has failed or passed. If it has failed, it will reveal the reason behind the failure, which helps in identifying issues early in the development process and prevents them from becoming more serious problems later on. 3. Enables Frequent Testing Automated tests can be run on a regular basis, such as every time code changes are made, which can help identify issues early in the development process and prevent them from becoming more serious problems later on. This aids in catching bugs early in the development cycle and helps in reducing the overall cost of fixing bugs. import com.thoughtworks.gauge.Step
import org.openqa.selenium.By
import org.openqa.selenium.WebDriver
import org.openqa.selenium.chrome.ChromeDriver
import org.junit.jupiter.api.Test
import org.junit.jupiter.api.BeforeEach

class AutomationTest {
private lateinit var driver: WebDriver
@BeforeEach
fun openBrowser() {
    System.setProperty("webdriver.chrome.driver", "/path/to/chrome/driver")
    driver = ChromeDriver()
    driver.get("https://www.example.com")
}

@Test
fun testSearch() {
    driver.findElement(By.name("q")).sendKeys("searchTerm")
    driver.findElement(By.name("btnK")).click()
    val searchResult = driver.findElement(By.xpath("//div[@class='g']"))
    assert(searchResult.text.contains("searchTerm"))
}

@AfterEach
fun closeBrowser() {
    driver.quit()
  }
} This script uses JUnit5 framework and the annotation, which marks a method as a test method. This allows the test to be run automatically as part of a test suite. Additionally, the script uses the and annotations that marks a method to be executed before or after each test method. @Test @BeforeEach @AfterEach You can also use a CI/CD pipeline and integrate the automation test with it, allowing the tests to be run automatically as part of a build process and with each code change. This helps in ensuring that the code changes are tested and are of good quality before being deployed to the production. 4. Cost-effective Automated tests can run without human intervention, which reduces the cost of testing over time. Automated tests can be run on a regular basis and can be scheduled to run at a specific time which reduces the need for human resources. import com.thoughtworks.gauge.Step
import org.openqa.selenium.By
import org.openqa.selenium.WebDriver
import org.openqa.selenium.chrome.ChromeDriver
import org.junit.jupiter.api.Test
import org.junit.jupiter.api.BeforeEach

class AutomationTest {
private lateinit var driver: WebDriver

@BeforeEach
fun openBrowser() {
    System.setProperty("webdriver.chrome.driver", "/path/to/chrome/driver")
    driver = ChromeDriver()
    driver.get("https://www.example.com")
}

@Test
fun testSearch() {
    driver.findElement(By.name("q")).sendKeys("searchTerm")
    driver.findElement(By.name("btnK")).click()
    val searchResult = driver.findElement(By.xpath("//div[@class='g']"))
    assert(searchResult.text.contains("searchTerm"))
}

@Test
fun testSignUp() {
    driver.findElement(By.linkText("Sign Up")).click()
    driver.findElement(By.name("username")).sendKeys("myusername")
    driver.findElement(By.name("password")).sendKeys("mypassword")
    driver.findElement(By.name("submit")).click()
    val message = driver.findElement(By.xpath("//div[@class='message']"))
    assert(message.text.contains("Welcome myusername"))
}

@AfterEach
fun closeBrowser() {
    driver.quit()
  }
} In this example, we have two test cases. The first one is for testing search functionality and the second one is for testing the sign-up functionality. By having multiple test cases in a single script, it increases the overall efficiency of the testing process, which can help reduce the overall cost of testing. Additionally, you can use test parameterization where you can run the same test case with multiple inputs and data sets. This increases overall test coverage and helps find more bugs,  reducing the overall cost of testing. Further, you can use cloud-based test environments such as SauceLabs, BrowserStack, and TestingBot, allowing you to run your tests on a variety of browsers and operating systems without having to maintain your own test infrastructure. This can reduce the costs associated with maintaining and scaling test infrastructure. Overall, automated tests can be cost-effective by reducing the need for human resources, increasing the efficiency of the testing process, and by using cloud-based test environments. 5. Increases Coverage Automated tests can cover a wide range of scenarios, inputs and use cases which are difficult to cover manually, thus increasing the overall coverage of the testing. Automated tests can cover different browsers, different devices, different versions of the operating system, different data sets and different scenarios which increases the overall coverage of the testing. import com.thoughtworks.gauge.Step
import org.openqa.selenium.By
import org.openqa.selenium.WebDriver
import org.openqa.selenium.chrome.ChromeDriver
import org.junit.jupiter.api.Test
import org.junit.jupiter.api.BeforeEach
import org.junit.jupiter.params.ParameterizedTest
import org.junit.jupiter.params.provider.CsvSource

class AutomationTest {
private lateinit var driver: WebDriver

@BeforeEach
fun openBrowser() {
    System.setProperty("webdriver.chrome.driver", "/path/to/chrome/driver")
    driver = ChromeDriver()
    driver.get("https://www.example.com")
}

@ParameterizedTest
@CsvSource(value = ["searchTerm1, expectedResult1", "searchTerm2, expectedResult2", "searchTerm3, expectedResult3"])
fun testSearch(searchTerm: String, expectedResult: String) {
    driver.findElement(By.name("q")).sendKeys(searchTerm)
    driver.findElement(By.name("btnK")).click()
    val searchResult = driver.findElement(By.xpath("//div[@class='g']"))
    assert(searchResult.text.contains(expectedResult))
}

@AfterEach
fun closeBrowser() {
    driver.quit()
  }
} In this example, we are using the JUnit5 annotation and to run the same test case with multiple inputs, increasing the overall test coverage. The test will run three times with different input values, and each time it will check if the output matches the expected result. @ParameterizedTest @CsvSource This way you can test the same functionality with multiple data sets, which helps in finding more bugs and increases the overall test coverage. Additionally, you can also use data-driven testing frameworks like TestNG, JUnit5, etc. to run the test cases with multiple inputs,  increasing the overall test coverage. Automated tests run in the same way every time, ensuring the same issues are not missed repeatedly. Automated tests are repeatable, consistent and reliable which reduces false positives and false negatives. 6. Improves Consistency import com.thoughtworks.gauge.Step
import okhttp3.OkHttpClient
import okhttp3.Request
import okhttp3.Response

class API_AutomationTest {

@Step("GET request to <endpoint> and verify the response")
fun testAPI(endpoint: String) {
    val client = OkHttpClient()
    val request = Request.Builder()
        .url(endpoint)
        .get()
        .build()
    val response = client.newCall(request).execute()
    val json = response.body()?.string()
    assert(json!!.contains("\"userId\": 1"))
  }
} This script uses the Gauge framework and the annotation, which marks a method as a step in the test scenario. This allows the test scenario to be written in a readable and understandable format, making the flow of the test easy to understand. @Step You can also use the gauge's concept of data-driven testing, where you can run the same test scenario with multiple inputs, increasing the overall test coverage and potentially finding more bugs. Additionally, you can use the gauge's reporting capability, which provides a clear and concise report of the test scenarios, test results and the test execution time. 7. Enables Continuous Integration and Continuous Delivery Automated testing is a key enabler for continuous integration and delivery, which is a critical aspect of modern software development. Automated tests can be integrated with the CI/CD pipeline and can be run automatically on every code change, ensuring the code changes are tested and are of good quality before being deployed to the production. You can use a CI/CD pipeline and integrate the script with it, allowing the tests to be run automatically as part of a build process, and with each code change. For example, you can use Jenkins, Travis, CircleCI, etc. as a CI/CD tool. Then you can set up the job to build the project, run the test cases and deploy the application to the production if the tests pass. This is a critical aspect of modern software development, and it helps in achieving a faster and more reliable software delivery. pipeline {
    agent any
    stages {
        stage('Build') {
            steps {
                sh './gradlew build'
            }
        }
        stage('Test') {
            steps {
                sh 'gauge run specs/'
            }
        }
        stage('Deploy') {
            steps {
                sh './deploy.sh'
            }
        }
    }
} This Jenkinsfile defines a pipeline with three stages: Build, Test and Deploy. In the , the pipeline runs the command './gradlew build'. This command will build the project and generate the necessary artifacts. Build stage In the , the pipeline runs the command 'gauge run specs/'. This command will execute all the gauge test cases in the specs folder. Test stage In the , the pipeline runs the command './deploy.sh'. This command will deploy the application to the production environment. Deploy stage You can set up the Jenkins job to run this Jenkinsfile and it will build, test and deploy the application if the tests pass. With this example, you can see how you can use the Gauge tests in a Jenkinsfile to enable continuous integration and continuous delivery. This allows you to automate the testing process. Final Thoughts Increases efficiency by allowing multiple test cases to be run in a single script. Improves accuracy by reducing human error and providing consistent results. Enables frequent testing by making it easy to run tests on a regular basis. Is cost effective by reducing the overall cost of testing and increasing test coverage. Increases coverage by allowing the same test case to be run with multiple inputs and data sets. Improves consistency by ensuring that the software is working as expected. Enables Continuous integration and Continuous delivery by allowing the tests to be run automatically as part of a build process, and with each code change, ensuring the code changes are tested and are of good quality before being deployed to the production. Overall, automation testing helps in improving the overall quality of the software, reducing the overall cost of testing, increasing the test coverage, improving consistency and enabling continuous integration and continuous delivery which results in faster and more reliable software delivery.

7 Reasons to Use Automated Tests in Selenium, JUnit5, and Gauge Framework 

7 razones para usar pruebas automatizadas en Selenium, JUnit5 y Gauge Framework

7 raisons d'utiliser des tests automatisés dans Selenium, JUnit5 et Gauge Framework

सेलेनियम, JUnit5 और गेज फ्रेमवर्क में स्वचालित परीक्षण का उपयोग करने के 7 कारण

Selenium、JUnit5、Gauge Framework で自動テストを使用する 7 つの理由

7 razões para usar testes automatizados em Selenium, JUnit5 e Gauge Framework

7 lý do nên sử dụng Kiểm thử tự động trong Selenium, JUnit5 và Gauge Framework

在 Selenium、JUnit5 和 Gauge Framework 中使用自动化测试的 7 个理由

Tech is both the future and the road that will get us there. Whether it’s an adjacent digital reality- that integrates seamlessly with a tangible one- or an app that tracks your heart rate, we depend more and more on innovative solutions to problems we didn’t even know we had. The 2022 Noonies’ Award Category, presented by ,  puts the spotlight on the builders that improve our lives on a daily basis, the creative minds that help build these solutions, and by doing so light our path to an exciting future. Programming Slogging Here are the stats: 4,743 nominees. 371 Hacker Noon awards for 2022 Across 5 award categories: Web3, Emerging Tech, Gaming, Internet Heroes, and Programming. And a monumental 143,156 votes cast over at noonies.tech To see the winners simply visit , pick an award, and discover which name appears at the top. That’s the winner! noonies.tech The category alone is announcing 110 winners. Let’s congratulate the winners! Programming HackerNoon Contributor of the Year- JAVA is a Software Engineer and published author currently working as a Developer Advocate at MariaDB Corporation. Alejandro Duarte HackerNoon Contributor of the Year- KUBERNETES Aside from being a Content Strategist at Valasys Media, is  a Psychic, Tarot Reader and Violet Flame Alchemist. Priya Kumari HackerNoon Contributor of the Year- CODING is a Technical Leader with a hobby of competitive programming. Ruslan Rakhmedov HackerNoon Contributor of the Year- SQL is a Software Engineer and published author currently working as a Developer Advocate at MariaDB Corporation. Alejandro Duarte HackerNoon Contributor of the Year- DEVOPS Aside from being a Content Strategist at Valasys Media, is  a Psychic, Tarot Reader and Violet Flame Alchemist. Priya Kumari HackerNoon Contributor of the Year- CODING-SKILLS is a Senior Ruby on Rails Developer with 2+ YoE in developing e-commerce, travel, and blockchain projects. Alexey Shepelev HackerNoon Contributor of the Year- DATABASE is a Software Engineer and published author currently working as a Developer Advocate at MariaDB Corporation. Alejandro Duarte HackerNoon Contributor of the Year- CICD has 9+ years of professional experience as a Solutions architect, Tech lead, Cloud Architect and in DevOps. Viachaslau Matsukevich HackerNoon Contributor of the Year- CLOUD-COMPUTING Congratulations to , who loves coding in C# and Cloud Computing. Uladzislau Baryshchyk HackerNoon Contributor of the Year- AWS Congratulations to once again! Uladzislau Baryshchyk HackerNoon Contributor of the Year- MICROSERVICES brings over 19 years of experience in Software Development and a University degree in Computer Science. Oleg Melnic HackerNoon Contributor of the Year- GOLANG is a seasoned Software engineer Skilled in Go, Java, and JavaScript. Yuda Prasetiya Haqqy HackerNoon Contributor of the Year- CSS is a backend software engineer at Snap Inc,  interested in distributed systems, web security, and building cool things. Stanley Lim HackerNoon Contributor of the Year- PROGRAMMING is an Experienced Software Quality Assurance Tester at Libertex Group. Sergei Shaikin Coding Guru of 2022 is a Senior Software Engineer at Layermark. Alexander Makeev HackerNoon Contributor of the Year- MONITORING is a backend software engineer at Snap Inc,  interested in distributed systems, web security, and building cool things. Stanley Lim HackerNoon Contributor of the Year- GITHUB is a Software engineer with 11 years of experience focused on fault tolerant, distributed systems, PaaS, golang, HA. Anton Gubarev HackerNoon Contributor of the Year- DOCKER is a Software Engineer and published author, winning this year Noonies! Alejandro Duarte Tech Tutorial Teacher of the Year will solve all of your coding issues! Code basics HackerNoon Contributor of the Year- REACT is a Skilled Front-End engineer with 7 years of experience in developing Web and SmartTV applications. Yan Tsishko HackerNoon Contributor of the Year- FLUTTER is the Chief Technology Officer at Estatic Infotech Pvt. Ltd. Kiran Beladiya HackerNoon Contributor of the Year- API is an amazing data engineer and rockstar python teacher, winning this year's Noonies. Veronika Vasileva HackerNoon Contributor of the Year- CLEAN-CODE is a Senior software engineer & agile lover who specializes in declarative designs and S.O.L.I.D. Maximiliano Contieri HackerNoon Contributor of the Year- HOW-TO is a Senior Software Engineer at Akma Trading. Vladislav Gukasov HackerNoon Contributor of the Year- FUNCTIONAL-PROGRAMMING is a Staff Software Engineer at Lab49 with large hands-on experience on efficiently coding web apps. Georgii Kliukovkin Software Developer of The Year Asides managing Developer Experience & Relations at StepZen, is a Tech Author, Speaker and Entrepreneur. Roy Derks HackerNoon Contributor of the Year- SOFTWARE-ARCHITECTURE is a Senior Software Engineer with 7+ YoE building massively scalable systems both from scratch and diving into a codebase. Sergey Golitsyn HackerNoon Contributor of the Year- KOTLIN is a Senior Software Engineer at Ultimeta with over 10+ years of experience in software development with Java, Kotlin, and Spring. Fedor Yaremenko Top Programming Guru is a Principal Product Engineer at Esri. Hussein Nasser HackerNoon Contributor of the Year- JAVASCRIPT is a Frontend Developer and Technical Writer. Amarachi Iheanacho HackerNoon Contributor of the Year- GIT is a Software Engineer and published author currently working as a Developer Advocate at MariaDB Corporation. Alejandro Duarte iOS Writer of the Year Congratulations to , the Chief Technology Officer at Estatic Infotech Pvt. Ltd. Kiran Beladiya HackerNoon Contributor of the Year- DATA-SCIENCE Congratulations to , an avid data science writer currently studying Computer Science at Penn State University. Max Vidavsky HackerNoon Contributor of the Year- PRODUCT-DEVELOPMENT Congratulations to , the Chief Technology Officer at Estatic Infotech Pvt. Ltd. Kiran Beladiya Android Advocate of The Year Congratulations to once again!. Kiran Beladiya HackerNoon Contributor of the Year- SOFTWARE-TESTING Congrats to , a software engineer with a love for backend testing! Sergei Shaikin HackerNoon Contributor of the Year- MONGODB , Senior Ruby on Rails Developer, wins this award! Alexey Shepelev HackerNoon Contributor of the Year- LINUX The winner is , on his way to enlightenment! Aleksandr Tabakov HackerNoon Contributor of the Year- TESTING Congratulations to , Head of Product Marketing at Livecycle! Zevi Reinitz HackerNoon Contributor of the Year- DATA The award goes to… l, a Data Scientist, applied scientist, research consultant, freelancer and startup builder. Shaurya Uppa HackerNoon Contributor of the Year- DOTNET is the winner this year! Rahul Rai HackerNoon Contributor of the Year- DEBUGGING Congratulations to ! He is a professional fullstack software engineer, currently working for REVOLUT as a software backend engineer. Artem Sutulov HackerNoon Contributor of the Year- AUTHENTICATION is the winner! Rodrigo Kamada HackerNoon Contributor of the Year- FRONTEND Congratulations to , Frontend Developer And Technical Writer. Amarachi Iheanacho HackerNoon Contributor of the Year- AZURE Congratulations to , a solutions architect with expertise in public cloud, kubernetes, PAAS and CI\CD. Viachaslau Matsukevich HackerNoon Contributor of the Year- BACKEND wins his second award! Artem Sutulov HackerNoon Contributor of the Year- SERVERLESS is a solutions architect with expertise in public cloud, kubernetes, PAAS and CI\CD. Viachaslau Matsukevich HackerNoon Contributor of the Year- QUALITY-ASSURANCE is on fire! Congratulations! Ruslan Muradov HackerNoon Contributor of the Year- COMPUTER-SCIENCE Congratulations to ! Rishabh Agarwal HackerNoon Contributor of the Year- HEROKU , senior python developer, wins this award. Maksim Kuznetsov HackerNoon Contributor of the Year- REST-API Congratulations to ! Alexey Shepelev HackerNoon Contributor of the Year- NPM Congratulations to , Web/Blockchain Engineer, Technical Writer. Idris Abdul-Lateef HackerNoon Contributor of the Year- PERFORMANCE Congratulations to , a Senior Software Engineer. Yura Abharian HackerNoon Contributor of the Year- LOW-CODE , a passionate software engineer, wins this award. Aleksandr Zakharov HackerNoon Contributor of the Year- ANGULAR is the winner! Rodrigo Kamada HackerNoon Contributor of the Year- AGILE Congratulations to , a Software Engineering Manager at UTMOST, a Devops Advocate, passionate about Startups and continuous improvement pursuer Alessandro Diaferia HackerNoon Contributor of the Year- AGILE-SOFTWARE-DEVELOPMENT Congratulations to once again! Alessandro Diaferia Open Source Writer of the Year ’s motto is "EVERY DAY IS A LEARNING DAY". She is a Growing Developer, Python and Open-Source and a Tech Enthusiasts & Writer. Hillary Nyakundi Data Science Demon Congratulations to , a Data Scientist, a Problem Solving Expert and a Research Consultant. Shaurya Uppal HackerNoon Contributor of the Year- WEBSITE-DEVELOPMENT brings home another award! Rodrigo Kamada HackerNoon Contributor of the Year- OPTIMIZATION , a Data Engineer and a Python Tutor wins! Nikita Vasilev Master on the Edge of Computing Congratulations to for another win! Viachaslau Matsukevich HackerNoon Contributor of the Year- SOFTWARE-ENGINEERING Congratulations to ! Artem Sutulov HackerNoon Contributor of the Year- SCALING wins! Congratulations! Ralph Benko HackerNoon Contributor of the Year- CONTAINERS has been a pioneer in video software development since 2005. Congrats! Fora Soft HackerNoon Contributor of the Year- TYPESCRIPT , currently developing and enjoying life, and meanwhile winning Noonies. Anatolii Kabanov Rawr for ROR , a 6+ years Java Developer, crashed this award! Alexander Makeev HackerNoon Contributor of the Year- SWIFT ,  a senior IOS Software Engineer at BestDoctor has won the HackerNoon Contributor of the Year- Swift. Swift is designed to function as the intuitive language for ios, ipados, macos, watchos, and tvos. Pavel Plotnikov Most Authentic Developer Advocate of The Year has your back! Dev Advocate at DraftBit and ReactNative & Expo Enthusiast and now, the most authentic dev advocate of the year. Aman Mittal HackerNoon Contributor of the Year- NEURAL-NETWORKS The crux of deep learning algorithms: Neural Networks is taken over by , a researcher / engineer with 10+ YoE in deep learning, computer vision, and autonomous driving. Dmitrii Khizbullin HackerNoon Contributor of the Year- HTML Build better websites with , a Senior Ruby on Rails Developer who has won the HTML Contributor of the Year at HackerNoon. Alexey Shepelev HackerNoon Contributor of the Year- HACKING wins the HackerNoon Contributor of the Year: Hacking. They write tutorials on Cybersecurity and Bug Bounty on our website and HackerNoon. Kali Linux Tutorials HackerNoon Contributor of the Year- INFRASTRUCTURE Build better websites with , a Senior Ruby on Rails Developer who has won the HTML Contributor of the Year at HackerNoon. Alexey Shepelev HackerNoon Contributor of the Year- REDUX takes the award for Contributor of the Year- Redux. Redux is an open source javascript library, commonly used with libraries like react or angular for building user interfaces. Anatolii Kabanov No No No NodeJs wins their second title for NodeJs. Congratulations X2! Anatolii Kabanov HackerNoon Contributor of the Year- ENCRYPTION is an eater of bitcoin stories, spinner of blockchain tales, and lastly the winner of the HackerNoon Contributor of the Year- Encryption. StevieSats HackerNoon Contributor of the Year- REFACTORING This award is for the highest quality refactoring stories on hackernoon and it goes to . Abbey is a full-stack web developer, crafter, blogger, cosplayer, fiber artist, yoga teacher, and gamer. Abbey Perini HackerNoon Contributor of the Year- ENGINEERING is not only a Tech Enthusiast, he is also the HackerNoon Contributor of the Year for Engineering. Rishabh Agarwal HackerNoon Contributor of the Year- RASPBERRY-PI loves Robotics and IoT. Nazar has provided us with the highest quality raspberry-pi stories for HackerNoon and has won the HackerNoon Contributor of the year award. Chingiz Nazar HackerNoon Contributor of the Year- DEVELOPER-TOOLS is a founder at Adadot.com, the world’s first fitness tracker for work, helping developers improve the way they work and feel. His contributions for HackerNoon will be remembered forever. Congratulations Alex! Alex Harris Developer Defying Patriarchy (While Being an Awesome Developer) is a software Engineer at Mezmo and mentor for female tech-newbies and advocate of slaying the imposter syndrome dragon. Sullivan, the defier of patriarchy and the HackerNoon Contributor of the Year, Congratulations! Shanna Sullivan HackerNoon Contributor of the Year- NATURAL-LANGUAGE-PROCESSING is a coder and a writer, with a goal of becoming a streamer someday. Tan is surely a step closer after winning HackerNoon Contributor of the Year for Natural Language Processing. Liling Tan Bug Finder of the Year is the HackerNoon Bug Finder of the Year, reporting bugs and significant vulnerabilities in a tech project. Andrej is also a dedicated writer and digital evangelist. Andrej Kovacevic HackerNoon Contributor of the Year- OPEN-SOURCE is focused on the open source software supply chain to build a better digital future for all of us. We can all appreciate Sal for his contributions and congratulate them for winning the HackerNoon Contributor of the year for Open Source. Sal Kimmich AI Artist Award is a trained neural network called DALL·E that creates images from text captions for a wide range of concepts expressible in natural language. 2022 has seen AI drawing software proliferate like no other and Dall E is one of them. Congratulations! Dall E HackerNoon Contributor of the Year- CODE-QUALITY Whatever your metrics for judgment are, code quality stories on hackernoon provide pointers that ease your way. helps make your Python code easier to read, understand, and work with through automatic refactoring suggestions. Let's congratulate them for being the HackerNoon Contributor of the Year for Code Quality! Sourcery HackerNoon Contributor of the Year- MOBILE is the CTO and Co-Founder of @incorainc, where we can turn your ideas into products. Incorainc is THE website that caters to your short time span. Congratulations on being the HackerNoon Contributor of the Year for Mobile. Tetiana Stoyko Pythonistas' Paradise is THE paradise for pythonistas.  They are a professional provider of data protection solutions for enterprises. Congrats on being the HackerNoon Contributor of the Year! Vinchin Backup & Recovery HackerNoon Contributor of the Year- ARCHITECTURE is a full-stack engineer and architect. Michelangelo would be proud of Andronchik’s contribution and being the HackerNoon Contributor of the Year. George Andronchik The Skynet Award Unconcerned with a global takeover at the hands of AI, this award honors the writers, inventors, or companies that brought us closer to the singularity this year. , an AI research and deployment company with the mission to ensure that artificial general intelligence benefits all of humanity has won the Hackernoon’s Skynet Award. OpenAI HackerNoon Contributor of the Year- APPLICATION Application is a set of instructions encoded in the language a computer understands. This was best reflected by , a Smart Web Vulnerability Scanner and their contributions to HackerNoon. Congratulations on winning the HackerNoon Contributor of the Year for Applications. SmartScanner HackerNoon Contributor of the Year- SCRUM Scrum represents complex products, sustainability and adaptive solutions, in the constantly evolving work and technological landscape. , the principal data consultant at ThoughtWorks has proven themselves to be the HackerNoon Contributor of the Year- Scrum. Ryan Dawson HackerNoon Contributor of the Year- ENGINEERING-MANAGEMENT is a human specialist and former VPE at New Relic, Gremlin. Jade wins the award  for the highest quality engineering-management stories on HackerNoon Jade Rubick HackerNoon Contributor of the Year- FIREBASE Congratulations to , a software engineer at Udgama for winning the HackerNoon Contributor of the Year for Firebase. Snehal Narkar HackerNoon Contributor of the Year- PROJECT-MANAGEMENT Project managers are so meticulous that they remember all the details. is one of them and wins this award with grace! Jade Rubick The Calmer of Storms Debugging is often the most grueling sweat of one’s brow but it easier by providing open-source error tracking to monitor and fix bugs and crashes anywhere in your application in real time. Sentry makes HackerNoon Contributor of the Year- APP-DEVELOPMENT Congratulations to , a Layer-3 peer-to-peer network to securely exchange liquidity & facilitate trading, clearing, settlement, and compliance. Yellow Network SaaSiest CEO of the year , SaaSiest Ceo of Notion, a project management software designed to help members of a company and individual users coordinate objectives, working processes and so forth is the winner! Ivan Zhao Tip your Hat Tutorials - Best Tutorials has some of the best tutorials on backend development, deployment, frameworks and much more. DigitalOcean Blog HackerNoon Contributor of the Year- CODE crashed this award! Oscar Daum HackerNoon Contributor of the Year- DATA-ANALYSIS Congratulations to , a global data labeling company. Toloka HackerNoon Contributor of the Year- GOOGLE-CLOUD-PLATFORM Congratulations to , a Java programmer and blogger! Javin Paul HackerNoon Contributor of the Year- PHP is an experienced Full-Stack Developer and CEO of BootesNull. Surinder Rawat The Best Alternatives to Notepad++ is a highly configurable text editor built to make creating and changing any kind of text very efficient. It is included as "vi" with most UNIX systems and with Apple OS X. Vim HackerNoon Contributor of the Year- CSS3 We all need a Sensei to guide us through our learning journey and has been our most favorite in CSS3 voyage. Congratulations! Anderson Osayerie HackerNoon Contributor of the Year- SOFTWARE is a Chief Product Officer for Chargify and SaaSOptics. He is passionate about creating products that can maximize his customer’s ease. Barrow Hamilton HackerNoon Contributor of the Year- CMS is a passionate writer interested in new technologies, a dog lover and a digital nomad. Kat HackerNoon Contributor of the Year- DJANGO Congratulations to who is a Software engineer, blogger, lifelong learner with an added badge to his armor as this year’s DJANGO master . Ismail Tlemcani HackerNoon Contributor of the Year- INFORMATION-SECURITY Congratulations to for keeping us well informed on how to stay safe in the virtual world jasonhiggins HackerNoon Contributor of the Year- LARAVEL To the artisan that made learning Laravel easier for all of us. Congratulations ! Adam Schmideg

#Noonies2022 Awards: The List of Winners in the Programming Category

Let’s become the architects of a cleaner, greener world The cloud has a than the airline industry. With the increasing demand for software to drive commerce and industry, there is a greater urgency for data collection and storage, cloud computing, and AI to source electricity from greener energy sources and generate significantly fewer carbon emissions. bigger carbon footprint By shifting electricity consumption to times and places where electricity is cleanest, programming can mitigate climate change and ensure a cleaner and safer future for us and future generations. This is Carbon Aware Computing, and it has the potential to make software development a critical force in mitigating climate change by helping us choose and optimize carbon-free energy with climate-conscious code. Carbon Aware Computing 101 There aren’t many examples of carbon-aware computing out there - which is why events like __ __are significant in crowd-sourcing new ideas and accelerating innovation in software programming to build applications that are both climate-conscious and energy-responsive. Carbon Hack 22 Green Software Foundation __ __carbon awareness as the idea of doing more when more energy comes from low-carbon “clean” sources and doing less when more energy comes from high-carbon “dirty” sources. As developers, we can do so by measuring and responding to the carbon intensity of electricity (gCO2/kWh), which can help create code to reduce the amount of carbon emissions generated through software. discusses Carbon Computing Approaches & Application There are broadly 3 approaches: Running our software in a cleaner location; sourcing electricity from a region where there is a greater supply of cleaner energy (e.g., wind, solar, water) than others. Location Shifting: 2. Running our software at a cleaner time; when clean energy is being generated by wind, water, or solar power. Time Shifting: Running our software so it does more when electricity is clean and less when it’s dirty. Demand Shaping: Building carbon-aware applications is challenging, despite various data sources, partly because some of these sources are hard to navigate. The , a project of the Green Software Foundation’s Open Source Working Group, makes it easier. This API helps us identify, analyse and predict the carbon intensity (how green or dirty the electricity is) of our application at any given time and in any given place and create code that allows our software to pull clean energy based on location, run when the wind is blowing or enable systems to follow the sun’s rays. open source Carbon Aware API The API is extremely simple. If you visit you will see the web interface like so: https://grnsft.org/hack22/api We can hook it to APIs like and , or to our own datasets. For the duration of the Carbon Hack 22 (now until Nov 10th, 2022), the Carbon Aware API will be for developers to use. Watttime Electricty Maps hooked up to a WattTime data source What time is best to run my software? Time Shifting: When time shifting, you can use the API endpoint, providing it with the location you are interested in in the field. Currently, the location names are mapped to Azure region names. You also need to provide the time window during which you are able to move the job. The start time needs to be entered in the field, and the end of it in the field. The dates provided should be in the ISO 8601 format. /emissions/bylocations/best location time toTime You can directly request data from the API via a Curl request like so: curl -X 'GET' \

'https://carbon-aware-api.azurewebsites.net/emissions/bylocations/best?location=uksouth&time=2022-10-08T00%3A00%2B01%3A00&toTime=2022-10-08T23%3A59%2B01%3A00' \

-H 'accept: application/json' Or even just a normal HTTP GET request in the . browser As output, you will receive an array of JSON objects, with the WattTime region name for the location, time of the data, carbon intensity rating, and duration for which this data is valid (WattTime provides data in 5-minute intervals). This data will be the lowest carbon ratings for the requested location. This query looks at data from a single 24-hour window, so to get an accurate result you might want to request data for multiple days (e.g., by looking at the endpoint) and check if this is not a one-time drop in carbon intensity! /emissions/bylocations [

{

"location": "UK",

"time": "2022-10-08T00:45:00+00:00",

"rating": 351.98767912,

"duration": "00:05:00"

},

{

"location": "UK",

"time": "2022-10-08T00:40:00+00:00",

"rating": 351.98767912,

"duration": "00:05:00"

},

{

"location": "UK",

"time": "2022-10-08T00:35:00+00:00",

"rating": 351.98767912,

"duration": "00:05:00"

}

] Where is the best location to run my software? Location Shifting: When location shifting, you can use the same endpoint as previously ( ), but for this time, provide multiple locations you are interested in and a single time during which you are looking. The example query below has multiple locations provided, taking under consideration. You can provide it with a time window (similarly to time shifting) or a single time point instead, filling in the and fields. /emissions/bylocations/best uksouth, eastus, and francecentral time toTime You can directly request data from the API via a Curl request like so: curl -X 'GET' \

'https://carbon-aware-api.azurewebsites.net/emissions/bylocations/best?location=uksouth&location=eastus&location=francecentral&time=2022-10-08T09%3A00%2B01%3A00' \

-H 'accept: application/json' Or even just a normal HTTP GET request in the . browser As output, you will receive the same format of a response, with locations having the lowest carbon intensity, along with the time for that reading, the actual value of the carbon intensity, and the duration for which this data is valid. [

  {

    "location": "FR",

    "time": "2022-10-08T08:00:00+00:00",

    "rating": 242.21832558,

    "duration": "00:05:00"

  }

] How do I adjust user behaviour? Demand shaping: To implement demand shaping, use a combination of the above queries and others to determine how best to adjust the behaviour of your application. Imagine this! Imagine if you still carried out those energy-hogger ML training jobs... but only when the electricity grid powering them was running on renewables. Imagine if your washing machine only ran at the time your local grid was fully wind-powered. Imagine you wanted to run your CI pipeline simultaneously each day, but ensured it runs only in an availability zone (country) currently powered by green energy. In all these cases, you would still consume as much energy as before, but you would generate much fewer greenhouse gas emissions. Whether you are polling the Carbon Aware API, running it in a local container, or as a Command Line Tool (CLI), you can use the patterns of event-driven development to connect events in your app to triggers in the electricity grid. Your own local grid or a greener, distant one. Depending on your use cases, you can allow an entire device, computing job, or application to run exclusively on renewable energy, either by only running when the local grid is green or running only from zones currently powered by green energy. Instead of being mechanics who keep the engine of digital emissions running, let’s be the engineers who help slow it down while maybe even contributing to the rebuild. Carbon-aware computing is the next frontier of sustainable software engineering. Some of the most influential decision-makers in the industry are interested in reducing carbon emissions from software and showing that by lending their resources and contributing significant prize money to . Key players like Accenture, Avanade, Boston Consulting Group, Globant, Goldman Sachs, Intel Corporation, Thoughtworks, UBS and VMWare have their best and brightest in sustainable technology participating and are collectively contributing 100,000 USD in prize capital. Carbon Hack 22 It is now also the job of software engineers to build solutions that do less harm to both people and the planet. Carbon-aware computing is how current and future generations of software engineers can help achieve this goal. and to join nearly 350 hackers and 70 innovations in identifying and resolving barriers to software that reduces the carbon intensity of electricity. Explore the Carbon Aware API participate in Carbon Hack Written by Asim Hussain, Executive Director and Chairperson, Green Software Foundation

Our Code Is Harming The Planet, We Need Carbon Aware Design Patterns

Nuestro código está dañando el planeta, necesitamos patrones de diseño conscientes del carbono

Notre code nuit à la planète, nous avons besoin de modèles de conception sensibles au carbone

हमारा कोड ग्रह को नुकसान पहुंचा रहा है, हमें कार्बन जागरूक डिजाइन पैटर्न की आवश्यकता है

私たちの行動規範は地球に害を及ぼしています。炭素を意識した設計パターンが必要です

Nosso código está prejudicando o planeta, precisamos de padrões de design com consciência de carbono

Quy tắc của chúng tôi đang gây hại cho hành tinh, chúng tôi cần các mẫu thiết kế nhận biết carbon

我们的代码正在危害地球，我们需要碳感知设计模式

Using APIs and vCards To Make Startup Swag Cool Again 

200 Black Women in Tech to Follow on Twitter in 2022

200 mujeres negras en tecnología para seguir en Twitter en 2022

200 femmes noires en technologie à suivre sur Twitter en 2022

टेक में 200 अश्वेत महिलाएं 2022 में ट्विटर पर फॉलो करेंगी

2022 年に Twitter でフォローするテクノロジー業界の黒人女性 200 人

200 mulheres negras em tecnologia para seguir no Twitter em 2022

200 phụ nữ da đen trong công nghệ để theo dõi trên Twitter vào năm 2022

2022 年将在 Twitter 上关注 200 名科技黑人女性

Stories, News & Mentions about THOUGHTWORKS

Thoughtworks’s Stories on HackerNoon

Thoughtworks's News & Mentions