Alternatives to CanvasJS  logo

Alternatives to CanvasJS

Plotly.js, Highcharts, FusionCharts, JavaScript, and Git are the most popular alternatives and competitors to CanvasJS .
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What is CanvasJS and what are its top alternatives?

CanvasJS is a powerful JavaScript charting library that allows users to create interactive and visually appealing charts and graphs for their web applications. It offers a wide range of chart types, customizable themes, real-time updates, and support for both desktop and mobile devices. However, one of its limitations is the lack of a free open-source version, as it is a commercial product with licensing fees.

  1. Chart.js: Chart.js is a popular open-source JavaScript charting library that offers a simple yet versatile way to create various types of charts. Key features include responsiveness, plugin support, and a wide range of chart types. Pros: Free and open-source, easy to get started, extensive documentation. Cons: Limited customization options compared to CanvasJS.

  2. Highcharts: Highcharts is a widely used JavaScript charting library that allows users to create interactive and professional-looking charts. It offers a variety of chart types, extensive customization options, and support for real-time updates. Pros: Feature-rich, great documentation, good community support. Cons: Commercial product with licensing fees.

  3. amCharts: amCharts is a versatile JavaScript charting library that provides a wide range of interactive and customizable charts. It offers features such as dynamic data loading, export options, and plugin support. Pros: Rich set of features, easy to use, support for live data. Cons: Paid licenses required for certain features.

  4. Google Charts: Google Charts is a free JavaScript charting library provided by Google that enables users to create a variety of charts and graphs. It offers simple integration with Google services, a wide range of chart types, and customization options. Pros: Free to use, good for simple charts, great integration with Google ecosystem. Cons: Limited customization compared to other libraries.

  5. FusionCharts: FusionCharts is a comprehensive JavaScript charting library that offers a wide range of interactive and animated charts. It provides support for real-time updates, extensive customization options, and various plugins for additional functionality. Pros: Feature-rich, great customer support, vast range of chart types. Cons: Commercial product with pricing based on usage.

  6. Plotly: Plotly is a versatile charting library that provides interactive and visually appealing charts. It offers support for 2D and 3D charts, real-time updates, and robust data visualization capabilities. Pros: Open-source core library, strong community, great for scientific and data-driven applications. Cons: Steeper learning curve compared to simpler libraries.

  7. ECharts: ECharts is an open-source charting library developed by Apache that offers advanced data visualization capabilities. It provides support for large datasets, interactive features, and various chart types. Pros: Open-source, powerful for complex visualizations, good for big data. Cons: Limited documentation and community support compared to other libraries.

  8. D3.js: D3.js is a popular JavaScript library for manipulating documents based on data. While not specifically a charting library, it is widely used to create custom data visualizations and charts. Pros: Highly customizable, great for creating unique visualizations, open-source. Cons: Steep learning curve, more time-consuming compared to charting-specific libraries.

  9. ApexCharts: ApexCharts is a modern JavaScript charting library that offers a simple and intuitive way to create beautiful and interactive charts. It provides a range of customization options, support for dynamic data, and real-time updates. Pros: Free and open-source, easy to use, great for responsive design. Cons: Limited chart types compared to other libraries.

  10. TauCharts: TauCharts is a flexible and customizable charting library that provides support for a variety of chart types and interactive features. It offers custom themes, dynamic data loading, and easy integration with web applications. Pros: Open-source, flexible visualization options, good for exploratory data analysis. Cons: Limited documentation and fewer customization options compared to other libraries.

Top Alternatives to CanvasJS

  • Plotly.js
    Plotly.js

    It is a standalone Javascript data visualization library, and it also powers the Python and R modules named plotly in those respective ecosystems (referred to as Plotly.py and Plotly.R). It can be used to produce dozens of chart types and visualizations, including statistical charts, 3D graphs, scientific charts, SVG and tile maps, financial charts and more. ...

  • Highcharts
    Highcharts

    Highcharts currently supports line, spline, area, areaspline, column, bar, pie, scatter, angular gauges, arearange, areasplinerange, columnrange, bubble, box plot, error bars, funnel, waterfall and polar chart types. ...

  • FusionCharts
    FusionCharts

    It is the most comprehensive JavaScript charting library, with over 100+ charts and 2000+ maps. Integrated with all popular JavaScript frameworks and server-side programming languages. Create interactive JavaScript charts for your web and enterprise applications. ...

  • JavaScript
    JavaScript

    JavaScript is most known as the scripting language for Web pages, but used in many non-browser environments as well such as node.js or Apache CouchDB. It is a prototype-based, multi-paradigm scripting language that is dynamic,and supports object-oriented, imperative, and functional programming styles. ...

  • Git
    Git

    Git is a free and open source distributed version control system designed to handle everything from small to very large projects with speed and efficiency. ...

  • GitHub
    GitHub

    GitHub is the best place to share code with friends, co-workers, classmates, and complete strangers. Over three million people use GitHub to build amazing things together. ...

  • Python
    Python

    Python is a general purpose programming language created by Guido Van Rossum. Python is most praised for its elegant syntax and readable code, if you are just beginning your programming career python suits you best. ...

  • jQuery
    jQuery

    jQuery is a cross-platform JavaScript library designed to simplify the client-side scripting of HTML. ...

CanvasJS alternatives & related posts

Plotly.js logo

Plotly.js

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A high-level, declarative charting library
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PROS OF PLOTLY.JS
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    Bindings to popular languages like Python, Node, R, etc
  • 10
    Integrated zoom and filter-out tools in charts and maps
  • 9
    Great support for complex and multiple axes
  • 8
    Powerful out-of-the-box featureset
  • 6
    Beautiful visualizations
  • 4
    Active user base
  • 4
    Impressive support for webgl 3D charts
  • 3
    Charts are easy to share with a cloud account
  • 3
    Webgl chart types are extremely performant
  • 2
    Interactive charts
  • 2
    Easy to use online editor for creating plotly.js charts
  • 2
    Publication quality image export
CONS OF PLOTLY.JS
  • 18
    Terrible document

related Plotly.js posts

Tim Abbott
Shared insights
on
Plotly.jsPlotly.jsD3.jsD3.js
at

We use Plotly (just their open source stuff) for Zulip's user-facing and admin-facing statistics graphs because it's a reasonably well-designed JavaScript graphing library.

If you've tried using D3.js, it's a pretty poor developer experience, and that translates to spending a bunch of time getting the graphs one wants even for things that are conceptually pretty basic. Plotly isn't amazing (it's decent), but it's way better than than D3 unless you have very specialized needs.

See more

Here is my stack on #Visualization. @FusionCharts and Highcharts are easy to use but only free for non-commercial. Chart.js and Plotly are two lovely tools for commercial use under the MIT license. And D3.js would be my last choice only if a complex customized plot is needed.

See more
Highcharts logo

Highcharts

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A charting library written in pure JavaScript, offering an easy way of adding interactive charts to your web...
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PROS OF HIGHCHARTS
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    Low learning curve and powerful
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    Multiple chart types such as pie, bar, line and others
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    Responsive charts
  • 9
    Handles everything you throw at it
  • 8
    Extremely easy-to-parse documentation
  • 5
    Built-in export chart as-is to image file
  • 5
    Easy to customize color scheme and palettes
  • 1
    Export on server side, can be used in email
CONS OF HIGHCHARTS
  • 9
    Expensive

related Highcharts posts

Here is my stack on #Visualization. @FusionCharts and Highcharts are easy to use but only free for non-commercial. Chart.js and Plotly are two lovely tools for commercial use under the MIT license. And D3.js would be my last choice only if a complex customized plot is needed.

See more
FusionCharts logo

FusionCharts

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JavaScript Charts for Web & Mobile Dashboards
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PROS OF FUSIONCHARTS
    Be the first to leave a pro
    CONS OF FUSIONCHARTS
    • 1
      Not free

    related FusionCharts posts

    JavaScript logo

    JavaScript

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      Extended functionality to web pages
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      Relatively easy language
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      Executed on the client side
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      Relatively fast to the end user
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      Functional programming
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      Setup is easy
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      Its everywhere
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      Because I love functions
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      JavaScript is the New PHP
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      Like it or not, JS is part of the web standard
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      Expansive community
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      Everyone use it
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      Can be used in backend, frontend and DB
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      Most Popular Language in the World
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      Powerful
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      Can be used both as frontend and backend as well
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      For the good parts
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      No need to use PHP
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      Easy to hire developers
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      Agile, packages simple to use
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      Love-hate relationship
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      Photoshop has 3 JS runtimes built in
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      Evolution of C
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      It's fun
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      Hard not to use
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      Versitile
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      Can be used on frontend/backend/Mobile/create PRO Ui
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      1.6K Can be used on frontend/backend
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    CONS OF JAVASCRIPT
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      Can be ugly
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      HORRIBLE DOCUMENTS, faulty code, repo has bugs

    related JavaScript posts

    Zach Holman

    Oof. I have truly hated JavaScript for a long time. Like, for over twenty years now. Like, since the Clinton administration. It's always been a nightmare to deal with all of the aspects of that silly language.

    But wowza, things have changed. Tooling is just way, way better. I'm primarily web-oriented, and using React and Apollo together the past few years really opened my eyes to building rich apps. And I deeply apologize for using the phrase rich apps; I don't think I've ever said such Enterprisey words before.

    But yeah, things are different now. I still love Rails, and still use it for a lot of apps I build. But it's that silly rich apps phrase that's the problem. Users have way more comprehensive expectations than they did even five years ago, and the JS community does a good job at building tools and tech that tackle the problems of making heavy, complicated UI and frontend work.

    Obviously there's a lot of things happening here, so just saying "JavaScript isn't terrible" might encompass a huge amount of libraries and frameworks. But if you're like me, yeah, give things another shot- I'm somehow not hating on JavaScript anymore and... gulp... I kinda love it.

    See more
    Conor Myhrvold
    Tech Brand Mgr, Office of CTO at Uber · | 44 upvotes · 11.2M views

    How Uber developed the open source, end-to-end distributed tracing Jaeger , now a CNCF project:

    Distributed tracing is quickly becoming a must-have component in the tools that organizations use to monitor their complex, microservice-based architectures. At Uber, our open source distributed tracing system Jaeger saw large-scale internal adoption throughout 2016, integrated into hundreds of microservices and now recording thousands of traces every second.

    Here is the story of how we got here, from investigating off-the-shelf solutions like Zipkin, to why we switched from pull to push architecture, and how distributed tracing will continue to evolve:

    https://eng.uber.com/distributed-tracing/

    (GitHub Pages : https://www.jaegertracing.io/, GitHub: https://github.com/jaegertracing/jaeger)

    Bindings/Operator: Python Java Node.js Go C++ Kubernetes JavaScript OpenShift C# Apache Spark

    See more
    Git logo

    Git

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      Distributed version control system
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      Efficient branching and merging
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      Fast
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      Open source
    • 726
      Better than svn
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      Great command-line application
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      Simple
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      Free
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      Easy to use
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      Does not require server
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      Distributed
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      Small & Fast
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      Feature based workflow
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      Staging Area
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      Most wide-spread VSC
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      Easy branching and merging
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      Compatible
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      Rebase supported natively; reflog; access to plumbing
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      Light
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      Fast, scalable, distributed revision control system
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      Easy
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      Flexible, easy, Safe, and fast
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      CLI is great, but the GUI tools are awesome
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      Phinx
    CONS OF GIT
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      Hard to learn
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      Rebase hell
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      Ironically even die-hard supporters screw up badly
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      Doesn't scale for big data

    related Git posts

    Simon Reymann
    Senior Fullstack Developer at QUANTUSflow Software GmbH · | 30 upvotes · 9.9M views

    Our whole DevOps stack consists of the following tools:

    • GitHub (incl. GitHub Pages/Markdown for Documentation, GettingStarted and HowTo's) for collaborative review and code management tool
    • Respectively Git as revision control system
    • SourceTree as Git GUI
    • Visual Studio Code as IDE
    • CircleCI for continuous integration (automatize development process)
    • Prettier / TSLint / ESLint as code linter
    • SonarQube as quality gate
    • Docker as container management (incl. Docker Compose for multi-container application management)
    • VirtualBox for operating system simulation tests
    • Kubernetes as cluster management for docker containers
    • Heroku for deploying in test environments
    • nginx as web server (preferably used as facade server in production environment)
    • SSLMate (using OpenSSL) for certificate management
    • Amazon EC2 (incl. Amazon S3) for deploying in stage (production-like) and production environments
    • PostgreSQL as preferred database system
    • Redis as preferred in-memory database/store (great for caching)

    The main reason we have chosen Kubernetes over Docker Swarm is related to the following artifacts:

    • Key features: Easy and flexible installation, Clear dashboard, Great scaling operations, Monitoring is an integral part, Great load balancing concepts, Monitors the condition and ensures compensation in the event of failure.
    • Applications: An application can be deployed using a combination of pods, deployments, and services (or micro-services).
    • Functionality: Kubernetes as a complex installation and setup process, but it not as limited as Docker Swarm.
    • Monitoring: It supports multiple versions of logging and monitoring when the services are deployed within the cluster (Elasticsearch/Kibana (ELK), Heapster/Grafana, Sysdig cloud integration).
    • Scalability: All-in-one framework for distributed systems.
    • Other Benefits: Kubernetes is backed by the Cloud Native Computing Foundation (CNCF), huge community among container orchestration tools, it is an open source and modular tool that works with any OS.
    See more
    Tymoteusz Paul
    Devops guy at X20X Development LTD · | 23 upvotes · 8.9M views

    Often enough I have to explain my way of going about setting up a CI/CD pipeline with multiple deployment platforms. Since I am a bit tired of yapping the same every single time, I've decided to write it up and share with the world this way, and send people to read it instead ;). I will explain it on "live-example" of how the Rome got built, basing that current methodology exists only of readme.md and wishes of good luck (as it usually is ;)).

    It always starts with an app, whatever it may be and reading the readmes available while Vagrant and VirtualBox is installing and updating. Following that is the first hurdle to go over - convert all the instruction/scripts into Ansible playbook(s), and only stopping when doing a clear vagrant up or vagrant reload we will have a fully working environment. As our Vagrant environment is now functional, it's time to break it! This is the moment to look for how things can be done better (too rigid/too lose versioning? Sloppy environment setup?) and replace them with the right way to do stuff, one that won't bite us in the backside. This is the point, and the best opportunity, to upcycle the existing way of doing dev environment to produce a proper, production-grade product.

    I should probably digress here for a moment and explain why. I firmly believe that the way you deploy production is the same way you should deploy develop, shy of few debugging-friendly setting. This way you avoid the discrepancy between how production work vs how development works, which almost always causes major pains in the back of the neck, and with use of proper tools should mean no more work for the developers. That's why we start with Vagrant as developer boxes should be as easy as vagrant up, but the meat of our product lies in Ansible which will do meat of the work and can be applied to almost anything: AWS, bare metal, docker, LXC, in open net, behind vpn - you name it.

    We must also give proper consideration to monitoring and logging hoovering at this point. My generic answer here is to grab Elasticsearch, Kibana, and Logstash. While for different use cases there may be better solutions, this one is well battle-tested, performs reasonably and is very easy to scale both vertically (within some limits) and horizontally. Logstash rules are easy to write and are well supported in maintenance through Ansible, which as I've mentioned earlier, are at the very core of things, and creating triggers/reports and alerts based on Elastic and Kibana is generally a breeze, including some quite complex aggregations.

    If we are happy with the state of the Ansible it's time to move on and put all those roles and playbooks to work. Namely, we need something to manage our CI/CD pipelines. For me, the choice is obvious: TeamCity. It's modern, robust and unlike most of the light-weight alternatives, it's transparent. What I mean by that is that it doesn't tell you how to do things, doesn't limit your ways to deploy, or test, or package for that matter. Instead, it provides a developer-friendly and rich playground for your pipelines. You can do most the same with Jenkins, but it has a quite dated look and feel to it, while also missing some key functionality that must be brought in via plugins (like quality REST API which comes built-in with TeamCity). It also comes with all the common-handy plugins like Slack or Apache Maven integration.

    The exact flow between CI and CD varies too greatly from one application to another to describe, so I will outline a few rules that guide me in it: 1. Make build steps as small as possible. This way when something breaks, we know exactly where, without needing to dig and root around. 2. All security credentials besides development environment must be sources from individual Vault instances. Keys to those containers should exist only on the CI/CD box and accessible by a few people (the less the better). This is pretty self-explanatory, as anything besides dev may contain sensitive data and, at times, be public-facing. Because of that appropriate security must be present. TeamCity shines in this department with excellent secrets-management. 3. Every part of the build chain shall consume and produce artifacts. If it creates nothing, it likely shouldn't be its own build. This way if any issue shows up with any environment or version, all developer has to do it is grab appropriate artifacts to reproduce the issue locally. 4. Deployment builds should be directly tied to specific Git branches/tags. This enables much easier tracking of what caused an issue, including automated identifying and tagging the author (nothing like automated regression testing!).

    Speaking of deployments, I generally try to keep it simple but also with a close eye on the wallet. Because of that, I am more than happy with AWS or another cloud provider, but also constantly peeking at the loads and do we get the value of what we are paying for. Often enough the pattern of use is not constantly erratic, but rather has a firm baseline which could be migrated away from the cloud and into bare metal boxes. That is another part where this approach strongly triumphs over the common Docker and CircleCI setup, where you are very much tied in to use cloud providers and getting out is expensive. Here to embrace bare-metal hosting all you need is a help of some container-based self-hosting software, my personal preference is with Proxmox and LXC. Following that all you must write are ansible scripts to manage hardware of Proxmox, similar way as you do for Amazon EC2 (ansible supports both greatly) and you are good to go. One does not exclude another, quite the opposite, as they can live in great synergy and cut your costs dramatically (the heavier your base load, the bigger the savings) while providing production-grade resiliency.

    See more
    GitHub logo

    GitHub

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      Pull request and features planning
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      Just works
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      Github pages
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      Open source
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      Network effect
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    • 43
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      Branching
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      Developer Profiles
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      Great for collaboration
    • 24
      It's fun
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      Clean interface and good integrations
    • 22
      Community SDK involvement
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      Learn from others source code
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      Because: Git
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      Standard in Open Source collab
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      Newsfeed
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      Fast
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      Cloud SCM
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      Nice API
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      Graphs
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      Integrations
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      It's awesome
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      Remarkable uptime
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      CI Integration
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      Hands down best online Git service available
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      Version Control
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      Simple but powerful
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      Unlimited Public Repos at no cost
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      Free HTML hosting
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      Security options
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      Loved by developers
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      Ci
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      Leads the copycats
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      All in one development service
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      Free private repos
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      Free HTML hostings
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      Easy and efficient maintainance of the projects
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      Beautiful
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      Easy source control and everything is backed up
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      IAM integration
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      Very Easy to Use
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      Good tools support
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      Issues tracker
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    CONS OF GITHUB
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      Only 3 collaborators for private repos
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      Limited featureset for issue management
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      Does not have a graph for showing history like git lens
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      GitHub Packages does not support SNAPSHOT versions
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      No multilingual interface
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      Takes a long time to commit
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    related GitHub posts

    Johnny Bell

    I was building a personal project that I needed to store items in a real time database. I am more comfortable with my Frontend skills than my backend so I didn't want to spend time building out anything in Ruby or Go.

    I stumbled on Firebase by #Google, and it was really all I needed. It had realtime data, an area for storing file uploads and best of all for the amount of data I needed it was free!

    I built out my application using tools I was familiar with, React for the framework, Redux.js to manage my state across components, and styled-components for the styling.

    Now as this was a project I was just working on in my free time for fun I didn't really want to pay for hosting. I did some research and I found Netlify. I had actually seen them at #ReactRally the year before and deployed a Gatsby site to Netlify already.

    Netlify was very easy to setup and link to my GitHub account you select a repo and pretty much with very little configuration you have a live site that will deploy every time you push to master.

    With the selection of these tools I was able to build out my application, connect it to a realtime database, and deploy to a live environment all with $0 spent.

    If you're looking to build out a small app I suggest giving these tools a go as you can get your idea out into the real world for absolutely no cost.

    See more

    Context: I wanted to create an end to end IoT data pipeline simulation in Google Cloud IoT Core and other GCP services. I never touched Terraform meaningfully until working on this project, and it's one of the best explorations in my development career. The documentation and syntax is incredibly human-readable and friendly. I'm used to building infrastructure through the google apis via Python , but I'm so glad past Sung did not make that decision. I was tempted to use Google Cloud Deployment Manager, but the templates were a bit convoluted by first impression. I'm glad past Sung did not make this decision either.

    Solution: Leveraging Google Cloud Build Google Cloud Run Google Cloud Bigtable Google BigQuery Google Cloud Storage Google Compute Engine along with some other fun tools, I can deploy over 40 GCP resources using Terraform!

    Check Out My Architecture: CLICK ME

    Check out the GitHub repo attached

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    Python logo

    Python

    241.7K
    197.1K
    6.9K
    A clear and powerful object-oriented programming language, comparable to Perl, Ruby, Scheme, or Java.
    241.7K
    197.1K
    + 1
    6.9K
    PROS OF PYTHON
    • 1.2K
      Great libraries
    • 961
      Readable code
    • 846
      Beautiful code
    • 787
      Rapid development
    • 689
      Large community
    • 435
      Open source
    • 393
      Elegant
    • 282
      Great community
    • 272
      Object oriented
    • 220
      Dynamic typing
    • 77
      Great standard library
    • 59
      Very fast
    • 55
      Functional programming
    • 49
      Easy to learn
    • 45
      Scientific computing
    • 35
      Great documentation
    • 29
      Productivity
    • 28
      Easy to read
    • 28
      Matlab alternative
    • 23
      Simple is better than complex
    • 20
      It's the way I think
    • 19
      Imperative
    • 18
      Free
    • 18
      Very programmer and non-programmer friendly
    • 17
      Powerfull language
    • 17
      Machine learning support
    • 16
      Fast and simple
    • 14
      Scripting
    • 12
      Explicit is better than implicit
    • 11
      Ease of development
    • 10
      Clear and easy and powerfull
    • 9
      Unlimited power
    • 8
      It's lean and fun to code
    • 8
      Import antigravity
    • 7
      Print "life is short, use python"
    • 7
      Python has great libraries for data processing
    • 6
      Although practicality beats purity
    • 6
      Flat is better than nested
    • 6
      Great for tooling
    • 6
      Rapid Prototyping
    • 6
      Readability counts
    • 6
      High Documented language
    • 6
      I love snakes
    • 6
      Fast coding and good for competitions
    • 6
      There should be one-- and preferably only one --obvious
    • 6
      Now is better than never
    • 5
      Great for analytics
    • 5
      Lists, tuples, dictionaries
    • 4
      Easy to learn and use
    • 4
      Simple and easy to learn
    • 4
      Easy to setup and run smooth
    • 4
      Web scraping
    • 4
      CG industry needs
    • 4
      Socially engaged community
    • 4
      Complex is better than complicated
    • 4
      Multiple Inheritence
    • 4
      Beautiful is better than ugly
    • 4
      Plotting
    • 3
      If the implementation is hard to explain, it's a bad id
    • 3
      Special cases aren't special enough to break the rules
    • 3
      Pip install everything
    • 3
      List comprehensions
    • 3
      No cruft
    • 3
      Generators
    • 3
      Import this
    • 3
      It is Very easy , simple and will you be love programmi
    • 3
      Many types of collections
    • 3
      If the implementation is easy to explain, it may be a g
    • 2
      Batteries included
    • 2
      Should START with this but not STICK with This
    • 2
      Powerful language for AI
    • 2
      Can understand easily who are new to programming
    • 2
      Flexible and easy
    • 2
      Good for hacking
    • 2
      A-to-Z
    • 2
      Because of Netflix
    • 2
      Only one way to do it
    • 2
      Better outcome
    • 1
      Sexy af
    • 1
      Slow
    • 1
      Securit
    • 0
      Ni
    • 0
      Powerful
    CONS OF PYTHON
    • 53
      Still divided between python 2 and python 3
    • 28
      Performance impact
    • 26
      Poor syntax for anonymous functions
    • 22
      GIL
    • 19
      Package management is a mess
    • 14
      Too imperative-oriented
    • 12
      Hard to understand
    • 12
      Dynamic typing
    • 12
      Very slow
    • 8
      Indentations matter a lot
    • 8
      Not everything is expression
    • 7
      Incredibly slow
    • 7
      Explicit self parameter in methods
    • 6
      Requires C functions for dynamic modules
    • 6
      Poor DSL capabilities
    • 6
      No anonymous functions
    • 5
      Fake object-oriented programming
    • 5
      Threading
    • 5
      The "lisp style" whitespaces
    • 5
      Official documentation is unclear.
    • 5
      Hard to obfuscate
    • 5
      Circular import
    • 4
      Lack of Syntax Sugar leads to "the pyramid of doom"
    • 4
      The benevolent-dictator-for-life quit
    • 4
      Not suitable for autocomplete
    • 2
      Meta classes
    • 1
      Training wheels (forced indentation)

    related Python posts

    Conor Myhrvold
    Tech Brand Mgr, Office of CTO at Uber · | 44 upvotes · 11.2M views

    How Uber developed the open source, end-to-end distributed tracing Jaeger , now a CNCF project:

    Distributed tracing is quickly becoming a must-have component in the tools that organizations use to monitor their complex, microservice-based architectures. At Uber, our open source distributed tracing system Jaeger saw large-scale internal adoption throughout 2016, integrated into hundreds of microservices and now recording thousands of traces every second.

    Here is the story of how we got here, from investigating off-the-shelf solutions like Zipkin, to why we switched from pull to push architecture, and how distributed tracing will continue to evolve:

    https://eng.uber.com/distributed-tracing/

    (GitHub Pages : https://www.jaegertracing.io/, GitHub: https://github.com/jaegertracing/jaeger)

    Bindings/Operator: Python Java Node.js Go C++ Kubernetes JavaScript OpenShift C# Apache Spark

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    Nick Parsons
    Building cool things on the internet 🛠️ at Stream · | 35 upvotes · 4M views

    Winds 2.0 is an open source Podcast/RSS reader developed by Stream with a core goal to enable a wide range of developers to contribute.

    We chose JavaScript because nearly every developer knows or can, at the very least, read JavaScript. With ES6 and Node.js v10.x.x, it’s become a very capable language. Async/Await is powerful and easy to use (Async/Await vs Promises). Babel allows us to experiment with next-generation JavaScript (features that are not in the official JavaScript spec yet). Yarn allows us to consistently install packages quickly (and is filled with tons of new tricks)

    We’re using JavaScript for everything – both front and backend. Most of our team is experienced with Go and Python, so Node was not an obvious choice for this app.

    Sure... there will be haters who refuse to acknowledge that there is anything remotely positive about JavaScript (there are even rants on Hacker News about Node.js); however, without writing completely in JavaScript, we would not have seen the results we did.

    #FrameworksFullStack #Languages

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    jQuery logo

    jQuery

    190.7K
    67.3K
    6.6K
    The Write Less, Do More, JavaScript Library.
    190.7K
    67.3K
    + 1
    6.6K
    PROS OF JQUERY
    • 1.3K
      Cross-browser
    • 957
      Dom manipulation
    • 809
      Power
    • 660
      Open source
    • 610
      Plugins
    • 459
      Easy
    • 395
      Popular
    • 350
      Feature-rich
    • 281
      Html5
    • 227
      Light weight
    • 93
      Simple
    • 84
      Great community
    • 79
      CSS3 Compliant
    • 69
      Mobile friendly
    • 67
      Fast
    • 43
      Intuitive
    • 42
      Swiss Army knife for webdev
    • 35
      Huge Community
    • 11
      Easy to learn
    • 4
      Clean code
    • 3
      Because of Ajax request :)
    • 2
      Powerful
    • 2
      Nice
    • 2
      Just awesome
    • 2
      Used everywhere
    • 1
      Improves productivity
    • 1
      Javascript
    • 1
      Easy Setup
    • 1
      Open Source, Simple, Easy Setup
    • 1
      It Just Works
    • 1
      Industry acceptance
    • 1
      Allows great manipulation of HTML and CSS
    • 1
      Widely Used
    • 1
      I love jQuery
    CONS OF JQUERY
    • 6
      Large size
    • 5
      Sometimes inconsistent API
    • 5
      Encourages DOM as primary data source
    • 2
      Live events is overly complex feature

    related jQuery posts

    Kir Shatrov
    Engineering Lead at Shopify · | 22 upvotes · 2.1M views

    The client-side stack of Shopify Admin has been a long journey. It started with HTML templates, jQuery and Prototype. We moved to Batman.js, our in-house Single-Page-Application framework (SPA), in 2013. Then, we re-evaluated our approach and moved back to statically rendered HTML and vanilla JavaScript. As the front-end ecosystem matured, we felt that it was time to rethink our approach again. Last year, we started working on moving Shopify Admin to React and TypeScript.

    Many things have changed since the days of jQuery and Batman. JavaScript execution is much faster. We can easily render our apps on the server to do less work on the client, and the resources and tooling for developers are substantially better with React than we ever had with Batman.

    #FrameworksFullStack #Languages

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    Ganesa Vijayakumar
    Full Stack Coder | Technical Lead · | 19 upvotes · 4.9M views

    I'm planning to create a web application and also a mobile application to provide a very good shopping experience to the end customers. Shortly, my application will be aggregate the product details from difference sources and giving a clear picture to the user that when and where to buy that product with best in Quality and cost.

    I have planned to develop this in many milestones for adding N number of features and I have picked my first part to complete the core part (aggregate the product details from different sources).

    As per my work experience and knowledge, I have chosen the followings stacks to this mission.

    UI: I would like to develop this application using React, React Router and React Native since I'm a little bit familiar on this and also most importantly these will help on developing both web and mobile apps. In addition, I'm gonna use the stacks JavaScript, jQuery, jQuery UI, jQuery Mobile, Bootstrap wherever required.

    Service: I have planned to use Java as the main business layer language as I have 7+ years of experience on this I believe I can do better work using Java than other languages. In addition, I'm thinking to use the stacks Node.js.

    Database and ORM: I'm gonna pick MySQL as DB and Hibernate as ORM since I have a piece of good knowledge and also work experience on this combination.

    Search Engine: I need to deal with a large amount of product data and it's in-detailed info to provide enough details to end user at the same time I need to focus on the performance area too. so I have decided to use Solr as a search engine for product search and suggestions. In addition, I'm thinking to replace Solr by Elasticsearch once explored/reviewed enough about Elasticsearch.

    Host: As of now, my plan to complete the application with decent features first and deploy it in a free hosting environment like Docker and Heroku and then once it is stable then I have planned to use the AWS products Amazon S3, EC2, Amazon RDS and Amazon Route 53. I'm not sure about Microsoft Azure that what is the specialty in it than Heroku and Amazon EC2 Container Service. Anyhow, I will do explore these once again and pick the best suite one for my requirement once I reached this level.

    Build and Repositories: I have decided to choose Apache Maven and Git as these are my favorites and also so popular on respectively build and repositories.

    Additional Utilities :) - I would like to choose Codacy for code review as their Startup plan will be very helpful to this application. I'm already experienced with Google CheckStyle and SonarQube even I'm looking something on Codacy.

    Happy Coding! Suggestions are welcome! :)

    Thanks, Ganesa

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