Prior to developing Mango, Li also co-founded Medmonk, a technology platform that provides financial assistance to patients undergoing medical treatment. In his new role as CTO, Bryan will oversee ...

Kindred Announces Acquihire of Mango, Appoints Founder as CTO

Medmonk

medmonk

Meet the Winners of Startups in North and Central America

As the old saying goes, few things in life are worse than bringing a knife to a gunfight. As an experienced developer, I can tell you this is 100% true for the programming world as well. You have to stay relevant in an industry that’s changing faster than I’m writing this article. Also, you have so many different tasks to do. So, after years of surviving in this environment, I always keep my favorite toys close and loaded. For a silent and precise job, there’s my trustful C++. If I want to make a big data bang, I choose R, somewhat extravagant, but convincingly powerful. But if things get really messy, I take my JavaScript off the wall: it kept me alive in the past and it can still take some more abuse. No language is good or bad for everyone. It depends on you and the job you have to do. So let’s discuss whether you should always keep up with the fashion or stick to the good old stuff. This question is a bit trickier than it seems… Is every day a new beginning?.. Given the innovative nature of tech, discussing whether to learn another language may sound silly altogether. Indeed, if the world stood still, we would still be using clay tablets and abacuses. Thankfully, the desire to innovate leads to continuous advancements, necessitating constant skill upgrades. For instance, in the 1960s, communicating with computers involved punching holes in paper and feeding it into a machine. Today, computers fit in our pockets, and the only holes we encounter are in our budgets after buying them. Imagine the most advanced developer from the '60s trying to stay relevant without evolving their skills. Programming languages evolve faster than their creators. New requirements, ideas, and tools that radically change traditional approaches and practices emerge at a remarkable pace. Specialists who ignore this fact become as outdated as the languages they cling to. A prime example is COBOL, which, despite being popular in the '60s for business applications, has become nearly obsolete with technological advancements. Another reason to learn a new programming language is career prospects. Understanding that greater knowledge of the latest technologies increases their chances of landing high-paying jobs, some specialists enthusiastically study new languages. This effort is justified: knowing new technologies undoubtedly broadens job opportunities. Sometimes, new technologies are necessary even if the language itself remains relevant but the company changes its tech stack. In such cases, employees must either adapt or find new jobs. Typically, they choose to learn the new technology. Learning new languages is beneficial not only for practical reasons but also for creativity. It can offer new approaches to familiar tasks, expanding perspectives and potential. For instance, Symfony, a popular PHP framework, was inspired by the Java-based Spring framework. Many open-source library developers have taken ideas from libraries implemented in other languages, and you could be the next innovator. …Or is old real gold? But for the sake of truth let’s look the other way round at all the things we discussed in the previous chapter. Do you know when punch cards were last used? Just take a guess… And unless you cheated by googling the answer right now, your guess would probably be wrong. The right answer is 2014. That year, the last punch cards were used by voting machines in the state of Idaho during the general elections in the US. Or take COBOL, the language we smashed as obsolete just a few paragraphs ago. Well, as of 2017, according to Reuters, 43% of all banking systems were based on COBOL; 80% of in-person transactions employed COBOL and there were 220 billion lines of code written in COBOL that were still in use. So there is actually a (not so fine) line between a language’s irrelevance for modern systems and its obsolescence in general. What are the odds of a programming language that you know or are going to learn, becoming obsolete and useless anytime soon? There is no real answer to that, only guesses. Some authors estimate the average lifecycle of a programming language to lie anywhere between 14 and 18.5 years. Others say it’s closer to 25 years for dominant languages and 5-6 for more niche and exotic tools. But there is also one more point of view worth mentioning because it looks quite proven by history. It is based on the Lindy effect, best described by Nassim Nicholas Taleb in his book ‘Antifragile: Things That Gain from Disorder’: “If a book has been in print for forty years, I can expect it to be in print for another forty years. But, and that is the main difference, if it survives another decade, then it will be expected to be in print another fifty years. This, simply, as a rule, tells you why things that have been around for a long time are not "aging" like persons, but "aging" in reverse. Every year that passes without extinction doubles the additional life expectancy. This is an indicator of some robustness. The robustness of an item is proportional to its life!” In other words, languages that have been out there long enough will probably stay with us for some more decades. On the contrary, there is a chance that fancy new stuff may become obsolete quite soon – but that remains to be seen. Each year of a language being in active service adds to its chances of surviving longer. Think careers rather than languages So what should we do? Learn a language? A new one or an old one? Well, learning itself is always good. Research shows that nearly half of all developers consider learning a new language. According to JetBrains' "The State of Developer Ecosystem 2023" survey, over 40% of respondents expressed this desire. Developers working with JavaScript (49%), PHP and Python (45%), and C++ (44%) developers show the most interest in learning something new. Choosing the language you want to learn actually depends on your career track. If you are thinking about a stable job that could last quite long (but not forever), you may opt for something old and proven. Such opportunities may arise in science, manufacturing and traditional banks, i.e. institutions with long-lasting expensive machinery and/or infrastructure heavily dependent on legacy code. For instance, the majority of COBOL programmers were between 45 and 55 years old in 2017. Add seven years that passed since the research was published and you will see that many COBOL developers are entering their retirement age. If COBOL lasts as long as it is expected, this may be your chance to become a rare specialist of high value. But keep in mind that such a choice would require very profound knowledge and immaculate skill. Also, should something go wrong, your further job choice may be very limited unless you decide to change your profile. If you prefer the vibrant life of startups or the power of tech giants, you should choose something more modern and promising. Seeing what others are doing may give you a clue: for instance, check the up-to-date rankings of programming languages by RedMonk or TIOBE. Don’t take them too seriously, as any imaginable ranking is based on some degree of simplification, but they may still give you the big picture of what is going on. Also, the mentioned survey by JetBrains reveals which languages are being learned most often. This way, you're sure to learn a language with a future. Always get practical Knowledge gained without practical application quickly becomes outdated. Therefore, learning a new language just for ticking a box is pointless. Instead, consider how you can use it in your current job. If your company already uses the technology, it can be a great incentive to learn. Simply ask your manager or team lead to assign you tasks related to it, allowing you to solidify your knowledge through practice. If your work uses a fixed tech stack and there's no opportunity to learn something new, you can always create your own side project to explore new boundaries independently. Just make sure not to violate your employment contract while doing so. Even if you're just starting your way in development, the strategy remains the same: choose a technology you can use in real life (at work or in a side project). Pick a language with a bright future, not one the developer community shuns. Since you're just beginning your journey and not burdened by high salaries and expectations as a specialist, you can easily and quickly change your tech stack if the second language you learn appeals to you more than the first. You have greater flexibility in your choice, and it's worth taking advantage of this benefit. Just don't overdo it: too frequent changes in your tech stack and an overwhelming flow of knowledge can be detrimental. Find a balance between what you want and what you can manage, and you will surely achieve success. For some developers, especially those with many years of experience, my advice may seem obvious. But as the saying goes, "Repetition is the mother of learning," and no one has ever disproved the truth of these wise words. I hope my article will encourage those who have long wanted to but were too lazy to take action and motivate eager novices. I hope my advice proves useful and perhaps even inspires further professional growth. As the old saying goes, few things in life are worse than bringing a knife to a gunfight. As an experienced developer, I can tell you this is 100% true for the programming world as well. You have to stay relevant in an industry that’s changing faster than I’m writing this article. Also, you have so many different tasks to do. So, after years of surviving in this environment, I always keep my favorite toys close and loaded. For a silent and precise job, there’s my trustful C++ . If I want to make a big data bang, I choose R, somewhat extravagant, but convincingly powerful. But if things get really messy, I take my JavaScript off the wall: it kept me alive in the past and it can still take some more abuse. No language is good or bad for everyone. It depends on you and the job you have to do. So let’s discuss whether you should always keep up with the fashion or stick to the good old stuff. This question is a bit trickier than it seems… C++ JavaScript Is every day a new beginning?.. Is every day a new beginning?.. Given the innovative nature of tech, discussing whether to learn another language may sound silly altogether. Indeed, if the world stood still, we would still be using clay tablets and abacuses. Thankfully, the desire to innovate leads to continuous advancements, necessitating constant skill upgrades. For instance, in the 1960s, communicating with computers involved punching holes in paper and feeding it into a machine. Today, computers fit in our pockets, and the only holes we encounter are in our budgets after buying them. Imagine the most advanced developer from the '60s trying to stay relevant without evolving their skills. Programming languages evolve faster than their creators. New requirements, ideas, and tools that radically change traditional approaches and practices emerge at a remarkable pace. Specialists who ignore this fact become as outdated as the languages they cling to. A prime example is COBOL, which, despite being popular in the '60s for business applications, has become nearly obsolete with technological advancements. Another reason to learn a new programming language is career prospects. Understanding that greater knowledge of the latest technologies increases their chances of landing high-paying jobs, some specialists enthusiastically study new languages. This effort is justified: knowing new technologies undoubtedly broadens job opportunities. Sometimes, new technologies are necessary even if the language itself remains relevant but the company changes its tech stack. In such cases, employees must either adapt or find new jobs. Typically, they choose to learn the new technology. Learning new languages is beneficial not only for practical reasons but also for creativity. It can offer new approaches to familiar tasks, expanding perspectives and potential. For instance, Symfony, a popular PHP framework, was inspired by the Java-based Spring framework. Many open-source library developers have taken ideas from libraries implemented in other languages, and you could be the next innovator. …Or is old real gold? …Or is old real gold? But for the sake of truth let’s look the other way round at all the things we discussed in the previous chapter. Do you know when punch cards were last used? Just take a guess… And unless you cheated by googling the answer right now, your guess would probably be wrong. The right answer is 2014. That year, the last punch cards were used by voting machines in the state of Idaho during the general elections in the US. Or take COBOL, the language we smashed as obsolete just a few paragraphs ago. Well, as of 2017, according to Reuters , 43% of all banking systems were based on COBOL; 80% of in-person transactions employed COBOL and there were 220 billion lines of code written in COBOL that were still in use. So there is actually a (not so fine) line between a language’s irrelevance for modern systems and its obsolescence in general. were used were used Reuters Reuters billion What are the odds of a programming language that you know or are going to learn, becoming obsolete and useless anytime soon? There is no real answer to that, only guesses. Some authors estimate the average lifecycle of a programming language to lie anywhere between 14 and 18.5 years . Others say it’s closer to 25 years for dominant languages and 5-6 for more niche and exotic tools. But there is also one more point of view worth mentioning because it looks quite proven by history. It is based on the Lindy effect, best described by Nassim Nicholas Taleb in his book ‘Antifragile: Things That Gain from Disorder’ : “If a book has been in print for forty years, I can expect it to be in print for another forty years. But, and that is the main difference, if it survives another decade, then it will be expected to be in print another fifty years. This, simply, as a rule, tells you why things that have been around for a long time are not "aging" like persons, but "aging" in reverse. Every year that passes without extinction doubles the additional life expectancy. This is an indicator of some robustness. The robustness of an item is proportional to its life!” between 14 and 18.5 years between 14 and 18.5 years say say one more point of view one more point of view ‘Antifragile: Things That Gain from Disorder’ ‘Antifragile: Things That Gain from Disorder’ “If a book has been in print for forty years, I can expect it to be in print for another forty years. But, and that is the main difference, if it survives another decade, then it will be expected to be in print another fifty years. This, simply, as a rule, tells you why things that have been around for a long time are not "aging" like persons, but "aging" in reverse. Every year that passes without extinction doubles the additional life expectancy. This is an indicator of some robustness. The robustness of an item is proportional to its life!” In other words, languages that have been out there long enough will probably stay with us for some more decades. On the contrary, there is a chance that fancy new stuff may become obsolete quite soon – but that remains to be seen. Each year of a language being in active service adds to its chances of surviving longer. Think careers rather than languages So what should we do? Learn a language? A new one or an old one? Well, learning itself is always good. Research shows that nearly half of all developers consider learning a new language. According to JetBrains' " The State of Developer Ecosystem 2023 " survey, over 40% of respondents expressed this desire. Developers working with JavaScript (49%), PHP and Python (45%), and C++ (44%) developers show the most interest in learning something new. Choosing the language you want to learn actually depends on your career track. If you are thinking about a stable job that could last quite long (but not forever), you may opt for something old and proven. Such opportunities may arise in science, manufacturing and traditional banks, i.e. institutions with long-lasting expensive machinery and/or infrastructure heavily dependent on legacy code. The State of Developer Ecosystem 2023 The State of Developer Ecosystem 2023 For instance, the majority of COBOL programmers were between 45 and 55 years old in 2017. Add seven years that passed since the research was published and you will see that many COBOL developers are entering their retirement age. If COBOL lasts as long as it is expected, this may be your chance to become a rare specialist of high value. But keep in mind that such a choice would require very profound knowledge and immaculate skill. Also, should something go wrong, your further job choice may be very limited unless you decide to change your profile. between 45 and 55 years old between 45 and 55 years old If you prefer the vibrant life of startups or the power of tech giants, you should choose something more modern and promising. Seeing what others are doing may give you a clue: for instance, check the up-to-date rankings of programming languages by RedMonk or TIOBE . Don’t take them too seriously, as any imaginable ranking is based on some degree of simplification, but they may still give you the big picture of what is going on. Also, the mentioned survey by JetBrains reveals which languages are being learned most often. This way, you're sure to learn a language with a future. RedMonk RedMonk TIOBE TIOBE which languages which languages Always get practical Knowledge gained without practical application quickly becomes outdated. Therefore, learning a new language just for ticking a box is pointless. Instead, consider how you can use it in your current job. If your company already uses the technology, it can be a great incentive to learn. Simply ask your manager or team lead to assign you tasks related to it, allowing you to solidify your knowledge through practice. If your work uses a fixed tech stack and there's no opportunity to learn something new, you can always create your own side project to explore new boundaries independently. Just make sure not to violate your employment contract while doing so. Even if you're just starting your way in development, the strategy remains the same: choose a technology you can use in real life (at work or in a side project). Pick a language with a bright future, not one the developer community shuns. Since you're just beginning your journey and not burdened by high salaries and expectations as a specialist, you can easily and quickly change your tech stack if the second language you learn appeals to you more than the first. You have greater flexibility in your choice, and it's worth taking advantage of this benefit. Just don't overdo it: too frequent changes in your tech stack and an overwhelming flow of knowledge can be detrimental. Find a balance between what you want and what you can manage, and you will surely achieve success. For some developers, especially those with many years of experience, my advice may seem obvious. But as the saying goes, "Repetition is the mother of learning," and no one has ever disproved the truth of these wise words. I hope my article will encourage those who have long wanted to but were too lazy to take action and motivate eager novices. I hope my advice proves useful and perhaps even inspires further professional growth.

Gentlemen, Choose Your Weapons! Programming Languages You Should (Not?) Learn

Menere, kies julle wapens! Programmeringstale wat jy (nie?) moet leer nie

أيها السادة، اختاروا أسلحتكم! لغات البرمجة التي يجب (أو لا يجب؟) تعلمها

Κύριοι, επιλέξτε τα όπλα σας! Γλώσσες προγραμματισμού που πρέπει (όχι;) να μάθετε

Señores, ¡elijan sus armas! Lenguajes de programación que deberían (¿no?) aprender

Tuan-tuan, Pilih Senjata Anda! Bahasa Pemrograman yang Harus (Tidak?) Anda Pelajari

सज्जनहरू, आफ्नो हतियार छान्नुहोस्! तपाईंले सिक्नु पर्ने प्रोग्रामिङ भाषाहरू (होइन?)

Heren, kies uw wapens! Programmeertalen die u (niet?) zou moeten leren

Cavalheiros, escolham suas armas! Linguagens de programação que vocês deveriam (não?) aprender

Mga ginoo, Piliin ang Iyong Mga Armas! Mga Wika sa Programming Dapat Mo (Hindi?) Matuto

Beyler, Silahlarınızı Seçin! Öğrenmeniz Gereken (Öğrenmemeniz Gereken) Programlama Dilleri

Các quý ông, hãy chọn vũ khí của mình! Các ngôn ngữ lập trình bạn nên (không nên?) học

先生们，选择你的武器！你应该（不？）学习的编程语言

GitOps as a practice has been in use since 2017 when Alexis Richardson coined the term. It transformed DevOps and automation. If you look at its core principles, it extends DevOps by treating Infrastructure as Code (IaC). Your deployment configuration is stored in a version control system (a.ka. Git), providing a single source of truth for both dev and ops. As the framework’s adoption increased, in the cloud native space. Many agile teams because of familiarity with git-based workflow for release management of cloud native workloads. GitOps became the standard for continuous deployment adopt GitOps GitOps principles differ from the traditional CI & CD pipeline approach. In the last few years, the GitOps working group under CNCF formalized all the ideas developed around GitOps into a cohesive set of principles that have become . the GitOps Principles Declarative Versioned and Immutable Pulled automatically Continuously Reconciled The uses of GitOps helped organizations in the following aspects: Swifter deployment and more often Fast and easy disaster recovery Effortless credential management Improved developer experience GitOps created a standard practice that allowed engineers to focus on developing solutions rather than figuring out how to deploy them. However, as companies grow, they increase the rate of new features, and the risk of downtime/failures in production also increases. They face problems like the control of blast radius and minimal risk from recent releases. So, is there any way blast radius can be minimized while testing out releases to a subset of users? – Yes, there is a way through Progressive Delivery. Progressive Delivery The term was coined by , who talked about new software development practices beyond continuous delivery. Based on James Governor’s transcript on Progressive Delivery, we want to minimize the blast radius and control the delivery. This could be done by diverting some traffic to new deployment, measuring the success metrics, and then promoting the release to all users. Some of deployment strategies are , , and . What is Progressive Delivery and who coined the term? Progressive Delivery James Governor at RedMonk Canary Blue-Green A&B testing There are a lot of tools that allow us to implement Progressive Delivery. Azure DevOps, AWS App Mesh are the widely used proprietary tools, while ArgoCD and Flux are widely used open source tools. In this blog post, we shall focus on Flux & Flagger, which is an open source tool that is quite popular. Flux is a tool for keeping Kubernetes clusters in sync with sources of configuration (like Git repositories) and automating updates to the configuration when there is new code to deploy. What is Flux? is a Progressive Delivery tool that automates the release process for applications running on Kubernetes. Under the hood, both tools are built on top of a modular GitOps toolkit. It is the main reason why Flagger compliments Flux. What is Flagger? Flagger Typical Pipeline Let’s level-set how a CI/CD pipeline works, then we can talk about how Flux and Flagger fit in the picture. In a typical CI/CD pipeline, we push the latest images to the registry and config changes to a repository. Hereon the Ops person will correct the cluster state with the new config changes by applying a new config or upgrading the existing resources in the Kubernetes cluster. This also typically means that the ops person should know the changes that need to be made along with the context of those changes, and hence this manual process quickly becomes error-prone. This whole process also becomes time-consuming and hard to manage. There can be issues that might occur after applying the latest changes. We need to have a solid & spontaneous feedback loop on new releases. What if we could automate the whole process from deployment to production and have proper change management in place for application & infra configuration? Here comes Flux, which helps us automate image tag updates to git and reconciliation of clusters to the desired state as soon as new changes are pushed to the git repository. Flux Let’s put Flux in place to see how we resolve all those issues from a typical pipeline. Source: GitOps Flux is based on the Operator pattern. Operator pattern is software extension to Kubernetes that uses custom resources to manage application components is built on top of Kubernetes API. Installation of Flux Installation of Flux is straightforward. You need to install Flux CLI to run the bootstrapping process. The bootstrapping process will create a repository on GitHub (or any other git hosting service) and all required manifests for installation and connection to the git repository. Follow this doc to . Get started with Flux Reconciliation Flux keeps a constant watch on the changes in your repository. It doesn’t require any event to start the reconciliation loop. It allows you to configure the reconciliation loop at each component. You can have your git checked every 3 minutes and sync in 10 min, allowing you to stagger how reconciliation happens. Automate image updates to Git When Flux comes into the picture, it will start watching your image registry for new updates and push back to git for you. We don’t have to take care of updating the image IDs this time. This feature is not enabled while setting up the Flux. You can follow this . Automate image update to Git - Flux Source: Image reflector and automation controllers | Flux Secret Management Once you adopt GitOps, you need to find a way to manage the secrets that your application might require to communicate to other services within the Kubernetes cluster. You can’t simply store your application secrets inside the git repository, right? You might be thinking of different ways of encryption. For example, you can commit secrets to version control and enable Flux to decrypt them. Flux provides two guides to store secrets through and . Sealed Secrets Mozilla SOPS Application Delivery Unlike other options Flux natively supports Helm and uses native Helm library to deploy helm release onto the cluster. This means you can run on a cluster and it will show exactly how works. helm ls helm install Another important thing is Flux allows you to manage dependencies between HelmRelease CRDs or Kustomization CRDs. It enables you to control the load order of collections/groups of YAML files. It does not maintain the order in which individual YAML files are applied. Promote Release Flux can help you automate the process of promoting the release with GitHub Actions. Source: Promote Flux Helm Releases with GitHub Actions Webhooks Flux is, by design pull-based (i.e, identifies the changes directly from the source ) and good at managing drift in clusters because it is easier to correct the state of a cluster from inside rather than from outside where your tool doesn’t have a correct understanding of the current state of clusters. Suppose you want to make your pipeline responsive as a push-based or faster process. In that case, you can set up webhook receivers on git push that will trigger reconciliation. Alerting and notifications Flux can notify you about the resource statuses change as the health of the new app’s version. You can receive alerts about reconciliation failure in clusters and configure different mediums for reporting - channels such as Slack or embedded in git commit status. This helps inform the developer team whether the new version of the app was deployed and whether it is healthy or not. Source: Flux Slack Error Alerts Source: Setup Notifications | Flux Source: Notification Controller | Flux Authorization Methods Flux relies on the RBAC capabilities of Kubernetes and does not have its own authorization management. It uses Kubernetes RBAC for authentication or authorization. It can be a downside if you want to provide authorization using SSO. User Interface There is no UI for Flux. It does have an that is not in an active development state at the time of writing this. experimental UI Flagger So far, we have automated our delivery process to the cluster with alerts and notifications in case of any failure and unhealthy state of the cluster. Now, we will look at how Flagger integrates into this process and allows different deployment strategies. How Flagger helps in Progressive Delivery. With the best alerting and notification in place, we are not resilient to downtime due to new releases. How can we be sure of our mission-critical services work as expected? A bad release can cause colossal business value loss. For example, your team might want to test a new feature on a small sample of users, and if that feature performs well, it will be rolled out to all users. In order to do that without causing any hindrance to day-to-day activities. Flagger lets us automate the release process and reduces the risk of introducing a new release in production by gradually shifting traffic to the new release while measuring metrics and running conformance tests. Example of Progressive Delivery with Flagger. Source: stefanprodan/gitops-progressive-delivery Configuration Flagger is compatible with any CI/CD solutions, so it can be used with Flux, Jenkins, Carvel, Argo, etc. It supports various service mesh like App Mesh, Istio, Linkerd, Kuma, Open Service Mesh, or an ingress controller like Contour, Gloo, NGINX, Skipper, and Traefik. It has excellent compatibility with Linkerd and it’s reasonably easy to get started with canary release and metrics analysis. One of the important factors that come into the picture sometime i.e., Flagger doesn’t require replacing Deployment objects with any custom type. Deployment Strategies Flagger implements several deployment strategies which help you achieve the same objective which is shifting traffic gradually to a new version of the release. Some of the strategies are: Canary Releases A/B Testing Blue/Green Mirroring Blue/Green Check out . Flux CD official docs to know more about deployment strategies Metrics Flagger comes with built-in metrics and a Grafana dashboard for canary analysis. It exposes Prometheus metrics to dig more into the canary analysis. You can create custom metrics that can be used to do metrics analysis for release. That’s the beauty of it once Flagger validates service level objects like response time and any other metrics specific to the app, it promotes the release otherwise, it will be automatically rolled back with minimum impact to end-users. We’re not diving into the details of how the metrics template can be used in the analysis step. Manual Gating Not just metrics-based approval, you can perform manual gating to have more control over your canary analysis. There are different kinds of webhooks that you can leverage at each step of canary analysis, for example, confirm-rollout and conform-promotion. A flagger will halt the canary traffic shifting and analysis until the confirm webhook returns HTTP status 200. Flagger also comes with load testing that can generate traffic during analysis. You can read more about . Webhooks - Flagger Let’s look at developer experience for both tools. Developer Experience Flux and Flagger both have a high learning curve and a lot of functionality, which means more power and can sometimes overwhelm the developer. Both don’t have any UI. The setup experience is pretty straightforward. If you talk about logging experience, you might need to get your hands dirty in CLI; otherwise, in other tools, you might have a UI that will show you the current progress of deployment. This makes life easier for a lot of developers. Conclusion We look at both tools and how they fit in our CI/CD pipeline and help us deliver progressively. With the use of Flagger, we can split traffic into proportions, which helps in testing out new releases to a subset of users or even getting feedback. Whether a new release should be released or not to all users. I hope you learned how these tools fit into GitOps with Progressive Delivery practice. References and further reading: Stefan Prodan on Flux, Flagger and the Operator pattern GlooOps: Progressive delivery, the GitOps way GitOps Guide to GitOps

GitOps using Flux and Flagger 

Recently, some colleagues from work as well as friends reached out to me and asked me for . Especially, during COVID-19, many people want to take advantage of their lockdown time by picking up a new skill like coding. advice on how they could learn to code and where to start In this blogpost, I will . Next, I will give you my and finally and show you how I used Python for solving some of my own problems to get you excited to start learning. explain first why you should learn Python favourite resources on how to learn Python introduce some project ideas Why should you pick Python as your first programming language? Simplicity and Code Readability Python’s like in other programming languages scattered all around the code make it a lot easier to write and read Python code. With some simple core concepts and knowledge it is a . Because you do not have to concentrate on how to read or write python code, it makes it easy to learn, use, and remember. intuitive use of indentation instead of other confusing symbols very beginner friendly programming language, where a little bit of Python code will get you quite far Popularity & Versatility Python is , climbing from third place to tie for second in the more popular than ever latest ranking of programming language popularity published by the analyst firm RedMonk Python’s versatility is one of the major reasons for its ongoing popularity. Both as well as academics prefer it as their favorite language for data-crunching large companies like Google, Dropbox, and Instagram all rely heavily on Python: We are often asked about the reasons that particular languages rise or fall in usage, and there are several patterns that have tended to correlate with increased (or decreased) usage. One of those is versatility. Like the language it tied with in this run, Java, Python has continued to find a role for itself in new workloads and use cases, even as it faces more specialized competition in these arenas. This, as much as the language’s general accessibility and ease of use, is one of the most important factors in Python’s ability to not merely survive, but thrive. How to learn Python programming? Since everyone learns differently it is hard to give an all size fits all answer to this question. Since there are so many offerings out there, I will share some of my . favorite resources from which you can try and pick what you like the most 1. Start with the Complete Python Bootcamp: Go from zero to hero in Python 3 from José Portilla on Udemy for 10€ — 20€ I personally liked this course quite a lot as… the course which happen all the time on Udemy. Please If you figure out you do not like the course you can always get your money back up to 30 days after the purchase without any questions being asked! the course is and allows you to . José does a great job at explaining all basic concepts in a clear and easy way. only costs between 10€ to 20€ during discounts do not buy it for the full price! more interactive than a book learn at your own pace However, there are with choosing this course: also some drawbacks Buying it once and having unlimited time to finish the course really . If you feel that you need some more pressure maybe a paid course on another platform like Dataquest, Udacity or alike might be a better fit for youWhile José does a great job at explaining the core concepts, I found . All code is written in a jupyter notebook and I personally feel there’s more fun projects where you can apply your newly learned knowledge. Hence, my second resource suggestion. requires a lot of discipline and dedication to finish most of the capstone projects not super interesting or useful in daily life 2) Apply some of your basic knowledge and concepts with Automate the Boring Stuff from Al Sweigart (free) On this website you will learn how to use Python to allowing you to effortlessly perform useful and impressive feats of automation. write programs that do in minutes what would take you hours to do by hand, I really like Al’s website because… as a business student, I felt that and useful than the ones provided in the Udemy course of JoséAl examples are much more practical focuses on execution and you can really do some cool programs in very little amount of time If you prefer reading and learning, you can also learn all the basic concepts here. 3. Find ideas and learn how to use python more effectively in a business setting on Practical Business Python (free) 4. Learn more about recent Python trends and find sources of inspiration for your own projects and interests through the Talk Python To Me and Python Bytes Podcast (free) Both Podcasts from Michael Kennedy and Brian Okken are a great way to learn and . At the same time they have some very nice beginner friendly Podcasts that are always fun to listen to during your commute or work out. find out more about the latest trends in Python My own top 9 project recommendations resources and libraries to get you started I firmly believe that you and create a small “wow effect”. With Python’s popularity comes a of third-party modules that makes this really easy. enjoy programming the most when you can build scripts or programs that solve your own problems large and extensive standard library (built into the core language) as well as a thriving ecosystem There are already many cool project ideas out there and like this one on or this on GitHub. With the many available options let me briefly present you my top 10 favorite projects and ideas that should get you excited to learn Python: Real Python curated list of aweomse Python project 1. Start Coding in Google Colab Setting up Python on your own computer can be quite painful and annoying. If you want to . It provides a jupyter style notebook hosted on Google’s server. get started right away use Google Colab for free I use Colab for Machine Learning projects since you can use their powerful GPUs and TPUs for resource demanding machine learning projects for free! My use case: 2. Automate Programs with PyAutoGUI PyAutoGUI lets your Python scripts with other applications. Annoyed of having to do the same clicks all over again at work? A perfect use case for this library! control the mouse and keyboard to automate interactions With PyAuto I changed and rearranged +100 PowerPoint slides for my CEMS Business Project. My use case: 3. Grow your Instagram Account with InstaBot Do you want to increase your Instagram Followers or find some interesting stats about your posts? Use this library to . automate your Instagram account I automated and grew one of my instagram accounts from 0 to 83 followers in just two weeks with almost no time spent. My use case: 4. WebScraping with Requests + BeautifulSoup or Scrapy Are your favorite shoes sold out again before you could buy them? Or do you wonder how often a price changes? with Requests and BeautifulSoup. Build a simple Price Monitor I analysed listings on ImmobilienScout to understand whether most of them follow the new Berlin Rental Cap. My use case: 5. Automate Google Chrome with Selenium Like PyAutogui, Selenium allows you to and build some handy automations. Tired of swiping on Tinder, automate it with Selenium! automatically interact with your GoogleChrome Browser I used Selenium to automatically download bank statements from my broker to use PortfolioPerformance to monitor my investments. My use case: 6. Automatically send Emails with smptlib , such as receiving email reminders from your code, send a confirmation email to users when they create an account or use it for some automated reporting. Automate anything related to emails When I was freelancing at the Bertelsmann Stiftung, I built a simple script that grabbed the link and headline of the latest post I published for them on wordpress and send them an automated email about the nw post. My use case: 7. Build awesome Machine Learning/Data Web Apps with Streamlit Streamlit’s open-source app framework is the in a few lines of Python code. All for free. easiest way to create beautiful, performant web apps I used this web framework to deploy a machine learning model that I build that allows everyone to predict a price of used tractors. Checkout my web app . My use case: here 8. Build time series forecasts with Facebook’s Prophet Prophet is a powerful library for open sourced by Facebook- forecasting time series data I used it for fun to download my bank account movements and predict how much money I would have in a few years. Not very realistic or useful but some just for fun playing around! My use case: 9. Sentiment Analysis with VADER VADER (Valence Aware Dictionary and sEntiment Reasoner) is a lexicon and rule-based that is specifically attuned to sentiments expressed in social media, and works well on texts from other domains. Check out this built with streamlit. sentiment analysis tool cool demo My use case: I am currently using this library at work to generate more insights from customer feedback through surveys. Extremely, easy to apply and some outstanding results. 10. Analyze and manipulate millions of rows with Pandas Pandas is a fast, powerful, flexible and easy to use open source , built on top of the Python programming language. If you will ever work with data, you will get to love with pandas. data analysis and manipulation tool Whenever Excel or GSheet breaks down and I get annoyed how slow those programs are or if I want to do some more complex data manipulation, I use pandas. Pandas is truly amazing, 10 million rows isn’t really a problem for pandas. The library is highly optimized for dealing with large tabular datasets through its DataFrame structure. My use case: Summary As a business student as it is a very , where a little bit of Python code will get you quite far. and then through automation or other project ideas that solve some of your own problems. With the right projects you will impress other, deliver value to your company and feel motivated to keep on learning and enjoy programming. you should learn Python beginner friendly programming language Start with a simple course to learn the basic concepts immediately pick some projects to apply your newly learned knowledge

The Hows, Whats, And Whys To Learning Python As A Business Student

Python is one of the oldest mainstream programming languages, which is now gaining even more ground with a growing demand for big data analytics. Enterprises continue to recognize the importance of big data, and generated by big data and business analytics in 2019 proves it right. $189.1 billion Python is a general-purpose programming language, but it found a stronghold within the analytics world because of its low entry barrier and vibrant community. But why exactly is it so good for analytical applications? Below is a breakdown that might be useful for less tech-savvy decision makers who want to learn more about Python for their hiring and operational purposes. Python is accessible Readability and ease of use are among Python’s main advantages. Python is often cited as the best language to learn for beginners due to its easy-to-understand syntax. Such attribute is especially appealing to data science professionals as they often have no other motives to learn coding other than for data analysis. The ecosystem around Python is incredibly strong, and to confirm this, one simply needs to take a look at . This combined ranking analyzes Stack Overflow discussions as well as GitHub contributions to measure the popularity of a programming language based on how often people ask questions about the language and how active the open-source contributor community is. Throughout the last eight years, Python has never dropped out of top five most popular programming languages. the Redmonk Programming Language Rankings R is another widely used programming language leading the analytics niche, as it was originally developed for scientific and analytical purposes. While it gained a lot of momentum during the past five years, it’s starting to lose ground now, and Python has played an important role in that. Of course, Stack Overflow and GitHub are subjective measures, especially when we know that Python is more versatile than R. So, why are these metrics important for analytics and any sort of data processing initiatives? The ecosystem around Python allows developers to speed up the implementation of analytical software. For example, you can retrain marketing or sales professionals to be certified in Python so they can perform rudimentary tasks involving analytics. This also means that if you have a robust business operations structure, it’s safer to hire , as their expertise could also be repurposed in case your analytics initiative flops. Python developers Python is flexible Python is great for building analytical tools and applications that can be both customer-oriented and internal. At the same time, languages like R are less flexible because they were envisioned with a narrower purpose in mind. This is also the reason why Python specialists are relatively more accessible. There are more of them, and they tend to have a wider range of skills and a more versatile experience. According to ZipRecruiter, the average annual pay for a Python programmer in the United States is $113,737, while for an R developer it is $129,633. Python is diverse Nowadays, deep learning (DL) goes hand in hand with analytics, as learning from huge amounts of data provides unprecedented business value. While there is a plethora of ways to tap into DL, Python is the most accessible tool known to date. It features the largest amount of deep learning libraries than any other programming language used in data analytics. Another great advantage of Python is its visualization capabilities. Visualization and graphics are integral parts of data science projects, and thanks to such Python libraries as Matplotlib, making sense of data becomes convenient and accessible. Python works for teams This language is commonly used within different business departments (business operations, marketing, logistics, sales, etc.) for a variety of purposes. As a result, your company’s professionals may have been exposed to Python at some point. This is why building an analytical system from scratch is easier with Python expertise on board. If other departments have experience with Python, then it becomes a lot easier to integrate them into your analytics project. Python is community-driven Python has a massive following of dedicated professionals and hobbyists. As data science becomes a popular career choice, the community continues to grow and, as a result, more users tend to create new data science libraries. This makes Python more attractive for data analytics professionals, as there are many prebuild solutions for the majority of common tasks. The Python communities on Stack Overflow and Codementor keep expanding and driving the data analytics field further as a consequence. A quick Google search will most likely solve any newcomer’s question, and if not, your fellow data analytics professionals will always be more than happy to help. A data analytics favorite Over the last decade, Python earned a reputation of being the most accessible language for data analytics. Its core strengths are its gradual learning curve, ever-growing community and rich ecosystem. Moreover, its flexibility and snowballing popularity makes Python developers even more valuable for large enterprises. There is no doubt that Python will be gaining momentum in the years to come, expanding its engaged community.

Why Python Is Leading the Charge in Data Analytics

According to the latest Octoverse report by Github , Kotlin has seen 2.6x growth in contributions as of September 2018 and is already becoming the go-to programming language for Android development. Understand what has made it inevitable. got big when it acquired the backing from the search engine behemoth at It is now the official programming language for Android development besides Java and C++ and #4 most loved languages in the tech world. Kotlin Google I/O 2017. (Source: StackOverflow ) As one of the most modern, statically-typed, open-source programming languages, Kotlin can run seamlessly on Java Virtual Machine (JVM). It has similarity with Java, JavaScript and C++. So as a developer, if you know these languages, you can get started with Kotlin in a couple of hours. Initially, when Kotlin was rolled out, most experienced developers started giving it a try. However, since it has been made the official Android language, more students and developers are moving towards Kotlin to build apps. According to a RedMonk Report - Around 80% of Developers are using Kotlin to build Android apps. About 31% use it for backend/server-side applications, while another 31% said they used it for SDK/libraries. Source: Pusher.com JetBrains — The ‘Brain’ behind Kotlin Kotlin has been around 2010. developed it to solve their own, internal development & productivity challenges. They are a software development company, and their core area of expertise is — IDEs. The basis of an IDE is a programming language. The first version of Kotlin came out in early 2016. And, since then Kotlin has been witnessing exponential growth. JetBrains Why was Kotlin built? Almost 70% of JetBrain’s products are built with Java. Slowly and gradually the company was getting tired with the old fashioned ways of writing code. With Java, even a fundamental thing like “Hello World” would require any developer to write three lines of code whereas modern languages can do this in about three words. This means more repetitive work. What JetBrains needed was a new language compatible with Java that would translate to adding new features to existing apps without rewriting things from scratch, and that is less code. They tried Scala, but it was not as fast as they wanted it to be. That’s how they built Kotlin. Developers are gravitating towards Kotlin because it’s making them happier. If you are into Project Management and Team Communication, chances are you use The company has been one of the earliest adopters of Kotlin. ‘Basecamp’. A third of the Android app’s code was Kotlin-based back in May 2016 . What’s going on, you might ask? ( it’s now 100% Kotlin ) The code is better, users are happier (the ultimate goal for any developer) and so are the programmers. Source: Signal v/s Noise Dan Kim, the android guy at BaseCamp, found himself uttering ‘holy-shit’ quite a few times when he started writing code using Kotlin. A popup adapter was his first Kotlin class. He completed it in 86 lines. The code was concise, readable and more importantly, he had fun writing it. Less was, indeed, More. Next, while converting an existing helper class, he just reduced 94 lines of code. He had just fallen in love with a programming language and as a developer myself, I can tell you how hard it is to love a language. Here are some of his favourite Kotlin features which they regularly use at Basecamp. Interestingly, he also shares how Kotlin has made him a happy programmer. As developers, we want to use frameworks/languages to come up with creative solutions to business problems than just cranking up some boilerplate code. Here’s how he puts it - Writing code that’s concise, clear, and expressive makes me happy. Focusing on creative solutions to business problems, not boilerplate and ceremony, makes me happy. Feeling an intense motivation to learn, which was missing in the Java days, makes me happy. And that’s super important. Because being happy isn’t just good for the soul. It’s great for your programming skills too. The more capable and friendly your language is, the happier you are. The happier you are, the better code choices you make. The better code choices you make, the better habits you build. And the better habits you build, the better programmer you become! This is exactly what’s happened with Kotlin and me over the past year. And I’m a better programmer because of it. Read more about his thoughts on Kotlin . here Christina Lee is an Android Engineer, and she was one of the first, few people at Pinterest to try Kotlin for the She says, video pin feature. “working with it will bring a smile on your face.” Any organization/company that has been using Java for years can quickly find itself in a quagmire — whether to continue using Java or try Kotlin? Because Java can do everything that Kotlin can. Millions of apps, today, are still baked with Java and continue to function well. The difference is that Kotlin speeds up the development. How? With more straightforward and easier to read code. No more using that boilerplate code. Kotlin sure has a more beautiful syntax, but it’s not about the features and functionalities any more. These features ensure that you have a more productive development environment. The Kotlin-based feature was introduced to approx. 150M users a month. Similar to Dan’s views, Christina opines that — Kotlin is not a radical departure from Java, as both produce bytecode for the JVM. But what makes it so nice to work with is that it is essentially a version of Java where best programming practices have been built into the language, so it is much easier to do the right thing than it is to do the wrong thing. This allows a developer to code with speed and confidence and removes much of the rote work that had been required while using Java. Because coding, like writing, is an expressive art form, having a tool like this that allows you to express your thoughts clearly, quickly, and accurately is a satisfying phenomenon and one worth getting excited about. When you combine this with the industry-leading tooling and interoperability of the language and the thorough and thoughtful language leadership displayed by the team at JetBrains, it was difficult not to fall in love with it. Listen to her thoughts here Caviar app, the on-demand food delivery app owned by Square is also 100% Kotlin-based. When Valeriy Ovechkin joined the Caviar team as a software engineer, the app was 100% Java. He started experimenting with Kotlin as a ‘passion project’ and pitched to the ‘Square’ team that it will make the development faster. When the existing features were rewritten in Kotlin, there was no significant impact on the end-users. But for developers, it was a game-changer. With Java, coding can be slow, and results can be clunky at times. Valeriy, gradually, started writing the new features in Kotlin because it’s easier to learn and makes the whole experience delightful. Writing fewer lines of code is what makes developers stick around. “What is interesting with Kotlin is basically it leverages the existing skills of Java developers and finds the right tradeoffs to provide new features and provide things that make Java developers more efficient without going too far,” — Pivotal engineer Sébastien Deleuze According to Ovechkin, the code (for Caviar’s Android version) is smaller by 15–25%. That’s because — Kotlin allows you to write concise, smaller amounts of code which results in easier management. Developers can focus on more significant problems than routine tasks. From Uber to Atlassian, biggies across the globe are using Kotlin to power their apps, back-end etc. Its usage will only grow in 2019. Why a 100% Kotlin-based Android App is the best bet for your business? — Start small by rewriting an existing feature of your app and see how it goes. Have it as a side project and see how your users react. This means your entire product does not need to invest much in the beginning. It is possible because Low Risk Kotlin classes export a Java API which is similar to Java code. — Kotlin is backed by Google and has become the official language for developing Android apps. There are tons of resources, talks and community help available to solve your queries regarding anything you feel stuck at. Active support community — You don’t need to purchase Kotlin. It’s freely available. If you know Java and C++, you can quickly develop your MVP and start testing your idea before investing full-fledged. You can crank out the best MVP within limited time and budget. Open source — When your team has to code less, it becomes more productive. Use their time efficiently to work on bigger, better issues than just code usual, routine stuff. It saves their time. It helps them work faster and better. Efficient teams — Less code, less maintenance, fewer nightmares. Additionally, there are tons of IDEs available for your developers, let them choose the SDK/toolkit of their liking. Easy on maintenance — Ask any Android developer, and you’ll know the pain point I am talking about. Kotlin is 100% backwards compatible. Compatible with previous versions — The last thing you would want on your app is impaired performance. Kotlin helps avoid No more memory leaks memory leaks. — Old projects working on Java? Never mind. Continue working on them with Kotlin. Interoperability with Java Ending Notes We strongly suggest you start using Kotlin for your android apps. At , we too have shifted from Java to Kotlin for building products for our clients, and they have been more than happy with the result. Start taking small steps today to reap big benefits tomorrow. Solutelabs Cheers!

Musings: What the tech world saw in Kotlin

Here is the list of 8 best programming languages and frameworks that you should look for in the year 2019 based in various and stats & facts. In the contemporary world of constant advancements in technology, computers have reached everywhere. Writing programs for computers is one of the most crucial work profiles in the current IT market. Have you ever wondered what is an even more hectic task than this? It’s selecting an appropriate programmer to develop software for your company. For a non-technical person, it’s not easy to choose the best programmer out of the ocean of developers. Here is an image which shows the characteristics on the basis of which you should hire a . software development company After selecting the best software developer, the next step is to choose the language which you want to use. It is again a strenuous task for every enterprise who want to outsource this service because software can bring great business gains for you. Plenty of companies and this is evident by the findings represented in the image below: outsource web developers A number of companies rely on for delivering the service of software development. web development companies Every company has its own way of delivering their services. Therefore, as an aware client, you need to know which is the most suitable language as per your needs. Let’s take a glimpse at the most popular languages among developers. Here are the findings of report. It shows how many developers are using a particular developing language. GitHub It clearly shows that JAVA is the most preferred language. Although it is not a proof that Java is the best language to choose. Every language has its own pros and cons. Therefore, enterprises should focus on their individual needs. There are many other factors other than popularity which decides the most suitable programming language for your software. In this post, we are discussing the top programming languages of 2019 in detail. By reading the following descriptions, you can get a greater idea about languages in trend this year. Here is a list of top programming languages of 2019: 1. Javascript: Javascript is no doubt the most trending language these days. It plays a chief role in front-end development. It can be used to develop interactive web pages very conveniently. Most of the in the contemporary market prefer to use it. full-stack software developers Stack Overflow, a popular website which is mainly used by developers surveyed 100,000 developers in 2018 to find the most preferred language. Javascript topped the list, followed by HTML, CSS, SQL, and others. Here is the representation of their in the following chart: survey This year too, it has been expected that Javascript will top the list again. The reason behind this popularity is probably its compatibility with all browsers. Moreover, it is also used in server-side through node.js. 2. Python: Python has grown more than any other language in recent years. The major reason behind its growing popularity python is the as well as the elimination of semicolon as an indication of the end of the statement. Python is one of the or machine learning based web & mobile apps. In many ways, it is similar and different from other programming languages. rise of Artificial intelligence preferred languages for creating AI The picture above shows the traffic of different Python packages. Pandas which was introduced in 2011 is the fastest growing python package. It is a multi-purpose language which can be used by for data science as well as web designing. software product development companies It would not be wrong to say that the growth of data science has enhanced the development of python as a programming language. Moreover, Python is also replacing Javascript as a teaching language in institutes. 3. Java: This language has survived at the peak in the programming industry from the past 20 years. It is widely used for building enterprise scale web application. also rely on this language for programming. Android mobile app developers It is considered as one of the most stable languages. This is why it is the most preferred language by large enterprises. Another important factor which has kept its magic intact among is its independence from platforms. web development companies It is known for high cross-platform compatibility. The Java Virtual Machine (JVM) allows it to run on various devices and platforms. Most of the fortune 500 companies use Java to develop back-end applications. 4. PHP: It stands for Hypertext Preprocessor and is a popular scripting language found in 1995. Newer languages could not make any difference to the popularity of PHP frameworks. This is mainly because it kept on evolving throughout these years. Most of the frameworks are free of cost and provide strong security features. It is the best server-side programming language according to a survey by . w3techs.com It has over-the-top benefits like libraries and modules which assure dynamic software development. This is why most of the websites and content management systems are written in the PHP programming language. There are many , which use this language for creating enterprise grade web applications with varied complexity. PHP development companies 5. SQL: Out of 19 million developers in the world, 7 million are using SQL according to the . You can get an idea about the immense popularity of this language from these statistics. Evans Data Corporation The chief reason behind this is its amazing feature of managing data structures and information stored in databases. SQL stands for Structured Query Language and allows faster retrieving of a large number of database records. The secured functioning of this programming language assists in maintaining the integrity of databases. If you require proficient data management then choose a SQL developer for optimum outcomes. 6. Ruby: This language was developed in Japan in the mid-1990s. Ruby is a dynamic language which was constructed with an aim to simplify the programming task and making it more fun. When you look at the data of you will get an idea about the popularity of Ruby. RedMonk Ruby is at 8th position and at the top among other programming languages. The major reason behind this is the Ruby of rails framework which is a full-stack web development framework. 7. C/C++: When this programming language comes into the light, every can agree to the fact that old is gold. It was introduced in the 1970s and still manages to be in the top software writing languages. custom software developer It would not be wrong to state that C is the parent language of many modern languages. Including the popular Java is derived from its syntax. C++ is a more dynamic language because its code is type-checked before it gets actually executed. A major difference between these languages is that C does not supports encapsulation, polymorphism, and inheritance but C++ does support all of these. Basically, we can conclude that C is a subset of C++. 8. Swift: Do you know how many iOS apps are there? Here is a table constructed from the findings of : Lifewire You can see that there is heavy competition in this arena as there is a large number of applications in the apple store. If you want to outshine among them then undoubtedly you must choose the programming language ~ Swift. It is developed by Apple Inc and introduced in the market in 2014. This language is the best choice for the development of native iOS or MAC OS app. Apart from this, it is a beginners’ friendly language. Influenced by Python and ruby, it is faster and more secure than both of them. The biggest advantage for swift (majorly used by ) is its similarity with the English language due to less code requirement. iOS app developers Conclusion: We have seen in this post that every language has its own pros and cons. As a business enterprise, you can find a proficient web or as per your requirements anytime. The crucial step that you need to take is the finalizing of a language. mobile app developers The language should be best suited for your requirements and budgets. Therefore, plan your software development according to the advantages and disadvantages of every language. Make sure to complete this first step efficiently to gain maximum profits. <a href="https://medium.com/media/3c851dac986ab6dbb2d1aaa91205a8eb/href">https://medium.com/media/3c851dac986ab6dbb2d1aaa91205a8eb/href</a>

8 Top Programming Languages & Frameworks of 2019

This is part one of a series of blog posts detailing my journey as a mobile app developer getting into . I am primarily an iOS developer, and I’ve dubbed 2018 My Year of Machine Learning. Before deciding to begin this process, I was held back by quite a few barriers and misconceptions about what the field really is, what it would take to get started, and how much of a paradigm shift it would be. I hope that by the end of this blog post you will have some clarity on some of the major considerations in starting machine learning, as well as possible next steps for you to begin your own journey. machine learning As developers, we’ve been able to solve many problems using mobile apps: social networking, bill payments, food ordering and delivery, and e-commerce, to mention but a few. Machine learning presents us with various opportunities to solve very unique types of problems. For example, machine learning can be applied to business to predict stock prices, or to sports management to predict which quarterback the Cleveland Browns should draft next, or to health care to predict disease outbreaks and to automatically classify image data like scans and x-rays, etc. (Or in other cases: !) to identify hotdogs Now to the crux of this post: as a developer, how do I get started in machine learning? A few questions come to mind when considering the field of machine learning. These include: Do I need math to get started in machine learning? How much math? What language should I use? What framework should I learn? What development and debugging tools should I use? What courses should I take? Let’s go ahead and address each of these questions. Do I need math to get started in machine learning? Short answer: . Math is very important. However, this might be the wrong question. The relevance of having an extensive understanding of advanced (“beyond basic”) mathematical concepts really depends on what your goals going into the field are. If you’re interested in taking a more academic approach, studying advanced data science, and researching and building the underlying algorithm and frameworks, then yes, a solid understanding of linear algebra, statistics, multivariate calculus, and other mathematical concepts is definitely required. If your goal is mainly to get started in machine learning / data science in order to work in the industry (entry level), then an in-depth knowledge of math is not really required. A basic grasp of freshman-level statistics will be quite sufficient, as you will be able to utilize a lot of modern machine learning tools/libraries to do math. Of course, being strong in math will generally help you - the key message here is simply that it is not a requirement for getting started in machine learning. Just like an iOS developer can make use of AFNetworking library to make a highly functioning app without knowing the inner workings of the library, so can you as a machine learning developer make use of various libraries to make calculations. When you started iOS/Android development, you probably didn’t start with learning how to build your own networking library from scratch, so why take that approach with machine learning? You can always continue to learn and master more math as you go. yes With that being said, libraries are not everything. As a developer, you will have to know the different types of algorithms in machine learning, including when and how to use them to solve problems, to make excellent products; just like you currently do in mobile development. Not that bad, right? If math is not really a prerequisite, then what is? Data analysis. The most important skill required in order to begin to master machine learning as a beginner is the ability to gather data, prepare data, explore data, and visualize data. There are several resources that can help get you up to speed with data visualization, data wrangling, and data aggregation. For example, if you were doing machine learning in Python, you should consider learning Base Python, Pandas, or Matplotlib. Proficiency in data analysis will adequately prepare you to create production ready machine learning products. What language should I use? In mobile development, the platforms constrained us to which languages are available for development: Swift and Objective C for iOS, Java and Kotlin for Android. In machine learning, however, we face a delicate conversation about which language we should focus our efforts on: Python? or R? or C/C++? There are probably several opinions on the interwebs that swear that a particular language is the best for machine learning. Maybe Python? However, those kinds of assertions should not be taken as fact. It really depends on several factors, like what your background is (what languages you’re already familiar with), and, more importantly, what problem you’re trying to solve. Languages are just a means to an end. Notwithstanding, in my experience so far, Python seems to be the easiest language to get started with in machine learning. It is the most popular language, and has a lot of libraries and a vibrant developer community around it. What frameworks should I learn? Choosing a framework is one of the most challenging and confusing aspects of starting out in machine learning, as there are a plethora of very good choices to pick from. However, just like deciding on which language to use, deciding on which framework to use still really depends on the kind of problem you wish to solve. The best way to really approach this is to read about the various options that are available, their strengths, weaknesses, and most common use cases. Some of the popular frameworks are TensorFlow, Torch, Caffe2, Keras, Microsoft CNTK, Amazon Machine Learning, Theano, etc. TensorFlow for example, is an open source project developed by the Google Brain team, and is most appropriate for research and development. Caffe2 is a lightweight, modular, and scalable framework open sourced by Facebook. Caffe2 is a good fit for building applications; Caffe2 has a lot of quick-start tutorials on its website that will help you start developing fairly quickly. During my process so far, I’ve come across several articles that shed more light on things to consider when picking the right framework. You can take a look at these for further reading: deep learning Choosing an Open Source Machine Learning Library: TensorFlow, Theano, Torch, scikit-learn, Caffe — Altexsoft Top 10 Machine Learning Frameworks — Prompt Cloud A Look at Popular Machine Learning Frameworks — Redmonk What development and debugging tools should I use? This was not a particularly big concern for me. Depending on the language you’ve decided to use, there are several well-done development and debugging tools for machine learning. For example, if you’re working with Python, some popular development environments are PyCharm, PyDev, Spyder, and Jupyter Notebook. A basic text editor like Visual Studio Code or Atom can do the trick as well. You can try them out and see which one(s) you prefer. Of course, the same applies to other languages as well. What courses should I take? There are A LOT of courses, articles, talks, and books on machine learning; some for beginners, and others for the more advanced. As we’re focusing on getting started here, I’ll highlight some of the courses and resources I’ve found very helpful so far. Here they are: : This course is by far the most recommended (and positively/excellently reviewed) course for beginners in machine learning. Professor Andrew Ng is an accomplished individual in machine learning known for his key role in the Google Brain project. He is the co-founder of Coursera and Chief Scientist and Vice President at Baidu. This course includes videos, readings, quizzes, and graded assignments to go along with the lectures. And in case you’re still worried about your math background, this course also teaches the necessary math topics like linear regression and linear algebra. Self-paced and free too! 10/10 Machine Learning — Standford University & Coursera — Andrew Ng : A free 10-week course taught by Katie Malone and Sebastian Thurn. Just like every Udacity course (nanodegree), this course is very well structured. I haven’t taken this course yet, but the reviews are very encouraging. Introduction to Machine Learning — Udacity And if you prefer to take a more gradual approach, you can focus on integrating machine learning into your iOS and Android apps. Here are a few resources that can help you hit the ground running: iOS Developer: Introducing Core ML Core ML in depth Vision Framework: Building on Core ML Natural Language Processing and your Apps Android Developer: Google’s Vision on Machine Learning Machine Learning Everywhere: Android Apps with Intelligence — Margaret Maynard-Reid Neural Networks API Android Neural Networks API Sample Thanks for taking the time to read this post! I hope this has helped demystify machine learning for you, so you can begin creating amazingly intelligent solutions to world problems. Happy trails on your machine learning journey! ❤ Further reading: The real prerequisite for machine learning isn’t math, it’s data analysis — Sharp Sight Labs The Mathematics of Machine Learning — Wale Akinfaderin What is the best programming language for Machine Learning? — Christina Voskoglou Best Programming Language for Machine Learning — Jason Brownlee The Most Popular Language For Machine Learning Is … — Jean Francois Puget Top 10 machine learning frameworks — Big Data Made Simple What Machine Learning Can and Can’t Do — Mark Boyd 8 Fun Machine Learning Projects For Beginners — EliteDataScience

Beyond Apps: A Developer’s Guide to Getting Started in Machine Learning

Node.js Emerging as the Universal Development Framework for a Diversity of Applications Last year and at the beginning of this year, we asked you, Node.js users, to help us understand where, how and why you are using Node.js. We wanted to see what technologies you are using alongside Node.js, how you are learning Node.js, what Node.js versions you are using, and how these answers differ across the globe. Thank you to all who participated. 1,405 people from around the world (85+ countries) completed the survey, which was available in English and Mandarin. 67% of respondents were developers, 28% held managerial titles, and 5% listed their position as “other.” Geographic representation of the survey covered: 35% United States and Canada, 41% EMEA, 19% APAC, and 6% Latin and South America. There was a lot of incredible data collected revealing: , ways of using Node.js, and deployment locations. Users span a broad mix of development focus used with Node.js. There is a large mix of tools and technologies — although many have been using Node.js less than 2 years. Experience with Node.js is also varied , being used at least half of their development time. Node.js is a key part of the survey user’s toolkit The report also painted a detailed picture of the types of technologies being used with Node.js, language preferences alongside Node.js, and preferred production and development environments for the technology. “Given developers’ important role in influencing the direction and pace of technology adoption, surveys of large developer communities are always interesting,” said Rachel Stephens, RedMonk Analyst. “This is particularly true when the community surveyed is strategically important like Node.js.” In September, we will be releasing the interactive infographic of the results, which will allow you to dive deeper into your areas of interest. For the time being, check out our blog on the report below and download the executive summary . here The Benefits of Node.js Grow with Time No Matter the Environment Node.js is emerging as a universal development framework for digital transformation with a broad diversity of applications. With more than 8 million Node.js instances online, three in four users are planning to increase their use of Node.js in the next 12 months. Many are learning Node.js in a foreign language with China being the second largest population outside of the United States using Node.js. Those who continue to use Node.js over time were more likely to note the increased business impact of the application platform. Key data includes: Most Node.js users found that the application platform helped improve developer satisfaction and productivity, and benefited from cost savings and increased application performance. The growth of Node.js within companies is a testament to the platform’s versatility. It is moving beyond being simply an application platform, and beginning to be used for rapid experimentation with . It is often times the primary focus for developers with the majority of developers spending their time with Node.js on the back-end, full stack, and front-end. Although Node.js use is beginning to rise in the Ops/DevOps sector and mobile as well. corporate data, application modernization and IoT solutions Node.js Used Less Than 2 Years, but Growing Rapidly In Businesses Experience with Node.js varied — although many have been using Node.js less than 2 years. Given the rapid pace of Node.js adoption with a growth rate of about 100% year-over-year this isn’t surprising. Companies that were surveyed noted that they were planning to expand their use of Node.js for web applications, enterprise, IoT, embedded systems and big data analytics. Conversely, they are looking to decrease the use of Java, PHP and Ruby. Large Mix of Technology and Tools Used Alongside Node.js for Digital Transformation Modernizing systems and processes are a top priority across businesses and verticals. Node.js’ light footprint and componentized nature make it a perfect fit for microservices (both container and serverless based) for lean software development without the need to gut out legacy infrastructure. The survey revealed that 47% of respondents are using Node.js for container and serverless-based architectures across development areas: 50% of respondents are using containers for back-end development. 52% of respondents are using containers for full-stack development. 39% of respondents are using containers for front-end development. 48% of respondents are using containers for another area of development with Node.js. The use of Node.js expands well beyond containers and cloud-native apps to touch development with databases, front-end framework/libraries, load balancing, message systems and more. And for developers of all focus areas, Node.js has versatile usage. Amazon Leads Serverless Technology and Node.js 68% of survey respondents who are using Node.js for serverless are using Amazon Web Services for production. 47% of survey respondents using Node.js and serverless are using Amazon Web Services for development. Developers who use Node.js and serverless used it across several development areas with the most popular being: back-end, full stack, front-end and DevOps. The Big Data Benefit Revenues for big data and business analytics are set to grow to more than . Vendors in this market require distributed systems within their products and rely on Node.js for data analysis. $203 billion in 2020 The survey revealed that big data/business analytics developers and managers are more likely to see major business impacts after instrumenting Node.js into their infrastructure with key benefits being productivity, satisfaction, cost containment, and increased application performance. Node.js Grows in the Enterprise With the creation of the in 2015, there has been an increase of enterprise development work with Node.js. The Long-Term support plan provides a stable (LTS) release line for enterprises that prioritize security and stability, and a current release line for developers who are looking for the latest updates and experimental features. The survey revealed: long-term support plan 39% of respondents with the developer title are using Node.js for enterprise. 59% of respondents with a manager title are using Node.js for enterprise. 69% of enterprise users plan to increase their use of Node.js over the next 12 months. 47% of enterprise users have been using Node.js for 3+ years and 58% of developers using Node.js have 10+ years in total development experience. The Long-Term Support versions of Node.js tend to be the most highly sought after with development. *Node.js 4 and 6 are the LTS versions and best suited for the enterprise of those that favor stability and security over new features. If you want to continue to learn more about Node.js, sign up for our monthly , which will continue to pepper you with data for months to come, and also provide you with information on cool new projects using Node.js. The Node.js Foundation will also hold its annual conference from October 4–6 in Vancouver, Canada. Come join us at . community newsletter Node.js Interactive *Mark Hinkle, the Node.js Foundation’s Executive Director, will be talking about this data and provide an update on the Foundation during his keynote at NodeSummit .

Node.js Emerging as the Universal Development Framework for a Diversity of Applications

“Lambda” by xTom at DeviantArt There’s no question that interest in functional programming (or FP) has grown significantly in recent years. However, it is not the functional renaissance that FP advocates have been claiming. Let’s look at the evidence… FP languages in general have not become popular, according to all the major language rankings. At , none of the top 20 languages are FP. At , Haskell is at #16 and Clojure at #20; no other top 20 language is FP. At , we don’t see a FP language until Haskell at #25. At , Haskell is at #20. In the , none of the top 12 Most Popular Technologies are FP. So where are all these FP languages??? TIOBE Redmonk IEEE Spectrum PYPL StackOverflow Developer Survey of 2016 Scala is not a FP language; it’s multi-paradigm with support for both FP and OOP. Scala has attained a measure of popularity by being a “better Java,” not by being FP. JavaScript is not a FP language; it’s a language with (weak) support for FP, as well as object prototypes. Its phenomenal popularity for web development has indeed raised the profile of FP, but otherwise this paradigm is hardly taking the world by storm. procedural While nearly all of the major languages (Java, Python, C#, C++, PHP, Ruby) have added first-class functions and closures to their feature set, no one uses these languages in a heavily FP manner. Lambdas exist as a convenience for those who care to write some FP code. How many colleges and universities even teach FP languages? MIT have taught programming to freshmen using Scheme (and ) for decades, but otherwise evidence of FP in CS curricula is scarce. SICP In fact, functional programming is generally harder for people to wrap their heads around. It’s hard for them to relate to the notion that everything in their world can be modelled as “functions.” A function is a relation between a set of inputs and a set of permissible outputs with the property that each input produces exactly one output. What the heck is a function??? Okay, I see…not. It’s far more intuitive for people to model their world as “objects.” Everybody knows what an object is. You can look at it, touch it, point to it to show someone else. A kindergarten child knows what an object is. Despite the benefits of functional programming, it is not the panacea that FP fans would have you believe. FP and OOP are merely tools that have their appropriate uses. FP is not a universal solution. It is also not free from downsides, for example, see and and . this this this It’s worth noting that Smalltalk, despite being a “pure” object-oriented language, has always had lambdas. Lambdas, in combination with objects, are one of the reasons Smalltalk is such a powerful and productive language. Yes, Smalltalk is not a FP language in the usual sense, but there is more than one way to skin the “functional programming” cat. Meow.

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