Alternatives to Amazon EC2 logo

Alternatives to Amazon EC2

Amazon LightSail, Amazon S3, Amazon EC2 Container Service, Beanstalk, and JavaScript are the most popular alternatives and competitors to Amazon EC2.
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What is Amazon EC2 and what are its top alternatives?

It is a web service that provides resizable compute capacity in the cloud. It is designed to make web-scale computing easier for developers.
Amazon EC2 is a tool in the Cloud Hosting category of a tech stack.

Top Alternatives to Amazon EC2

  • Amazon LightSail
    Amazon LightSail

    Everything you need to jumpstart your project on AWS—compute, storage, and networking—for a low, predictable price. Launch a virtual private server with just a few clicks. ...

  • Amazon S3
    Amazon S3

    Amazon Simple Storage Service provides a fully redundant data storage infrastructure for storing and retrieving any amount of data, at any time, from anywhere on the web ...

  • Amazon EC2 Container Service
    Amazon EC2 Container Service

    Amazon EC2 Container Service lets you launch and stop container-enabled applications with simple API calls, allows you to query the state of your cluster from a centralized service, and gives you access to many familiar Amazon EC2 features like security groups, EBS volumes and IAM roles. ...

  • Beanstalk
    Beanstalk

    A single process to commit code, review with the team, and deploy the final result to your customers. ...

  • 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. ...

Amazon EC2 alternatives & related posts

Amazon LightSail logo

Amazon LightSail

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Simple Virtual Private Servers on AWS
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PROS OF AMAZON LIGHTSAIL
  • 4
    Low cost
  • 4
    Simple Deployment
  • 1
    Simple pricing scheme
CONS OF AMAZON LIGHTSAIL
    Be the first to leave a con

    related Amazon LightSail posts

    Paul Whittemore
    Developer and Owner at Appurist Software · | 4 upvotes · 279.4K views

    For those needing hosting on Windows or Windows Server too (and avoiding licensing hurdles), both Vultr and Amazon LightSail offer compelling choices, depending on how much compute power you need. Don't underestimate Amazon LightSail, especially for smaller or starting projects, but Vultr also offers an incremental $16 Windows option on top of their standard compute offerings.

    See more
    Amazon S3 logo

    Amazon S3

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    Store and retrieve any amount of data, at any time, from anywhere on the web
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    PROS OF AMAZON S3
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      Reliable
    • 492
      Scalable
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      Cheap
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      Simple & easy
    • 83
      Many sdks
    • 30
      Logical
    • 13
      Easy Setup
    • 11
      REST API
    • 11
      1000+ POPs
    • 6
      Secure
    • 4
      Plug and play
    • 4
      Easy
    • 3
      Web UI for uploading files
    • 2
      Faster on response
    • 2
      Flexible
    • 2
      GDPR ready
    • 1
      Easy to use
    • 1
      Plug-gable
    • 1
      Easy integration with CloudFront
    CONS OF AMAZON S3
    • 7
      Permissions take some time to get right
    • 6
      Requires a credit card
    • 6
      Takes time/work to organize buckets & folders properly
    • 3
      Complex to set up

    related Amazon S3 posts

    Ashish Singh
    Tech Lead, Big Data Platform at Pinterest · | 38 upvotes · 3M views

    To provide employees with the critical need of interactive querying, we’ve worked with Presto, an open-source distributed SQL query engine, over the years. Operating Presto at Pinterest’s scale has involved resolving quite a few challenges like, supporting deeply nested and huge thrift schemas, slow/ bad worker detection and remediation, auto-scaling cluster, graceful cluster shutdown and impersonation support for ldap authenticator.

    Our infrastructure is built on top of Amazon EC2 and we leverage Amazon S3 for storing our data. This separates compute and storage layers, and allows multiple compute clusters to share the S3 data.

    We have hundreds of petabytes of data and tens of thousands of Apache Hive tables. Our Presto clusters are comprised of a fleet of 450 r4.8xl EC2 instances. Presto clusters together have over 100 TBs of memory and 14K vcpu cores. Within Pinterest, we have close to more than 1,000 monthly active users (out of total 1,600+ Pinterest employees) using Presto, who run about 400K queries on these clusters per month.

    Each query submitted to Presto cluster is logged to a Kafka topic via Singer. Singer is a logging agent built at Pinterest and we talked about it in a previous post. Each query is logged when it is submitted and when it finishes. When a Presto cluster crashes, we will have query submitted events without corresponding query finished events. These events enable us to capture the effect of cluster crashes over time.

    Each Presto cluster at Pinterest has workers on a mix of dedicated AWS EC2 instances and Kubernetes pods. Kubernetes platform provides us with the capability to add and remove workers from a Presto cluster very quickly. The best-case latency on bringing up a new worker on Kubernetes is less than a minute. However, when the Kubernetes cluster itself is out of resources and needs to scale up, it can take up to ten minutes. Some other advantages of deploying on Kubernetes platform is that our Presto deployment becomes agnostic of cloud vendor, instance types, OS, etc.

    #BigData #AWS #DataScience #DataEngineering

    See more
    Russel Werner
    Lead Engineer at StackShare · | 32 upvotes · 2.6M views

    StackShare Feed is built entirely with React, Glamorous, and Apollo. One of our objectives with the public launch of the Feed was to enable a Server-side rendered (SSR) experience for our organic search traffic. When you visit the StackShare Feed, and you aren't logged in, you are delivered the Trending feed experience. We use an in-house Node.js rendering microservice to generate this HTML. This microservice needs to run and serve requests independent of our Rails web app. Up until recently, we had a mono-repo with our Rails and React code living happily together and all served from the same web process. In order to deploy our SSR app into a Heroku environment, we needed to split out our front-end application into a separate repo in GitHub. The driving factor in this decision was mostly due to limitations imposed by Heroku specifically with how processes can't communicate with each other. A new SSR app was created in Heroku and linked directly to the frontend repo so it stays in-sync with changes.

    Related to this, we need a way to "deploy" our frontend changes to various server environments without building & releasing the entire Ruby application. We built a hybrid Amazon S3 Amazon CloudFront solution to host our Webpack bundles. A new CircleCI script builds the bundles and uploads them to S3. The final step in our rollout is to update some keys in Redis so our Rails app knows which bundles to serve. The result of these efforts were significant. Our frontend team now moves independently of our backend team, our build & release process takes only a few minutes, we are now using an edge CDN to serve JS assets, and we have pre-rendered React pages!

    #StackDecisionsLaunch #SSR #Microservices #FrontEndRepoSplit

    See more
    Amazon EC2 Container Service logo

    Amazon EC2 Container Service

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    Container management service that supports Docker containers
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    PROS OF AMAZON EC2 CONTAINER SERVICE
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      Backed by amazon
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      Familiar to ec2
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      Cluster based
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      Simple API
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      Iam roles
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      Scheduler
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      Cluster management
    • 7
      Programmatic Control
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      Container-enabled applications
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      Socker support
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      No additional cost
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      Easy to use and cheap
    CONS OF AMAZON EC2 CONTAINER SERVICE
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      related Amazon EC2 Container Service posts

      Eric Colson
      Chief Algorithms Officer at Stitch Fix · | 21 upvotes · 6.1M views

      The algorithms and data infrastructure at Stitch Fix is housed in #AWS. Data acquisition is split between events flowing through Kafka, and periodic snapshots of PostgreSQL DBs. We store data in an Amazon S3 based data warehouse. Apache Spark on Yarn is our tool of choice for data movement and #ETL. Because our storage layer (s3) is decoupled from our processing layer, we are able to scale our compute environment very elastically. We have several semi-permanent, autoscaling Yarn clusters running to serve our data processing needs. While the bulk of our compute infrastructure is dedicated to algorithmic processing, we also implemented Presto for adhoc queries and dashboards.

      Beyond data movement and ETL, most #ML centric jobs (e.g. model training and execution) run in a similarly elastic environment as containers running Python and R code on Amazon EC2 Container Service clusters. The execution of batch jobs on top of ECS is managed by Flotilla, a service we built in house and open sourced (see https://github.com/stitchfix/flotilla-os).

      At Stitch Fix, algorithmic integrations are pervasive across the business. We have dozens of data products actively integrated systems. That requires serving layer that is robust, agile, flexible, and allows for self-service. Models produced on Flotilla are packaged for deployment in production using Khan, another framework we've developed internally. Khan provides our data scientists the ability to quickly productionize those models they've developed with open source frameworks in Python 3 (e.g. PyTorch, sklearn), by automatically packaging them as Docker containers and deploying to Amazon ECS. This provides our data scientist a one-click method of getting from their algorithms to production. We then integrate those deployments into a service mesh, which allows us to A/B test various implementations in our product.

      For more info:

      #DataScience #DataStack #Data

      See more
      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

      See more
      Beanstalk logo

      Beanstalk

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      Private code hosting for teams.
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      PROS OF BEANSTALK
      • 14
        Ftp deploy
      • 9
        Deployment
      • 8
        Easy to navigate
      • 4
        Code Editing
      • 4
        HipChat Integration
      • 4
        Integrations
      • 3
        Code review
      • 2
        HTML Preview
      • 1
        Security
      • 1
        Blame Tool
      • 1
        Cohesion
      CONS OF BEANSTALK
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        related Beanstalk posts

        JavaScript logo

        JavaScript

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        Lightweight, interpreted, object-oriented language with first-class functions
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        PROS OF JAVASCRIPT
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          Can be used on frontend/backend
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          It's everywhere
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          Lots of great frameworks
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          Fast
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          Light weight
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          Flexible
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          You can't get a device today that doesn't run js
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          Non-blocking i/o
        • 237
          Ubiquitousness
        • 191
          Expressive
        • 55
          Extended functionality to web pages
        • 49
          Relatively easy language
        • 46
          Executed on the client side
        • 30
          Relatively fast to the end user
        • 25
          Pure Javascript
        • 21
          Functional programming
        • 15
          Async
        • 13
          Full-stack
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          Setup is easy
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          Future Language of The Web
        • 12
          Its everywhere
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          Because I love functions
        • 11
          JavaScript is the New PHP
        • 10
          Like it or not, JS is part of the web standard
        • 9
          Expansive community
        • 9
          Everyone use it
        • 9
          Can be used in backend, frontend and DB
        • 9
          Easy
        • 8
          Most Popular Language in the World
        • 8
          Powerful
        • 8
          Can be used both as frontend and backend as well
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          For the good parts
        • 8
          No need to use PHP
        • 8
          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
        • 7
          Evolution of C
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          It's fun
        • 7
          Hard not to use
        • 7
          Versitile
        • 7
          Its fun and fast
        • 7
          Nice
        • 7
          Popularized Class-Less Architecture & Lambdas
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          Supports lambdas and closures
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          It let's me use Babel & Typescript
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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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          Client side JS uses the visitors CPU to save Server Res
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          Easy to make something
        • 5
          Clojurescript
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          Promise relationship
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          Stockholm Syndrome
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          Function expressions are useful for callbacks
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          Scope manipulation
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          Everywhere
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          Client processing
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          What to add
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          Because it is so simple and lightweight
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          Only Programming language on browser
        • 1
          Test
        • 1
          Hard to learn
        • 1
          Test2
        • 1
          Not the best
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          Easy to understand
        • 1
          Subskill #4
        • 1
          Easy to learn
        • 0
          Hard 彤
        CONS OF JAVASCRIPT
        • 22
          A constant moving target, too much churn
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          Horribly inconsistent
        • 15
          Javascript is the New PHP
        • 9
          No ability to monitor memory utilitization
        • 8
          Shows Zero output in case of ANY error
        • 7
          Thinks strange results are better than errors
        • 6
          Can be ugly
        • 3
          No GitHub
        • 2
          Slow
        • 0
          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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        PROS OF GIT
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          Distributed version control system
        • 1.1K
          Efficient branching and merging
        • 959
          Fast
        • 845
          Open source
        • 726
          Better than svn
        • 368
          Great command-line application
        • 306
          Simple
        • 291
          Free
        • 232
          Easy to use
        • 222
          Does not require server
        • 27
          Distributed
        • 22
          Small & Fast
        • 18
          Feature based workflow
        • 15
          Staging Area
        • 13
          Most wide-spread VSC
        • 11
          Role-based codelines
        • 11
          Disposable Experimentation
        • 7
          Frictionless Context Switching
        • 6
          Data Assurance
        • 5
          Efficient
        • 4
          Just awesome
        • 3
          Github integration
        • 3
          Easy branching and merging
        • 2
          Compatible
        • 2
          Flexible
        • 2
          Possible to lose history and commits
        • 1
          Rebase supported natively; reflog; access to plumbing
        • 1
          Light
        • 1
          Team Integration
        • 1
          Fast, scalable, distributed revision control system
        • 1
          Easy
        • 1
          Flexible, easy, Safe, and fast
        • 1
          CLI is great, but the GUI tools are awesome
        • 1
          It's what you do
        • 0
          Phinx
        CONS OF GIT
        • 16
          Hard to learn
        • 11
          Inconsistent command line interface
        • 9
          Easy to lose uncommitted work
        • 7
          Worst documentation ever possibly made
        • 5
          Awful merge handling
        • 3
          Unexistent preventive security flows
        • 3
          Rebase hell
        • 2
          When --force is disabled, cannot rebase
        • 2
          Ironically even die-hard supporters screw up badly
        • 1
          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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        Powerful collaboration, review, and code management for open source and private development projects
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        PROS OF GITHUB
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          Open source friendly
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          Easy source control
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          Nice UI
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          Great for team collaboration
        • 867
          Easy setup
        • 504
          Issue tracker
        • 486
          Great community
        • 483
          Remote team collaboration
        • 451
          Great way to share
        • 442
          Pull request and features planning
        • 147
          Just works
        • 132
          Integrated in many tools
        • 121
          Free Public Repos
        • 116
          Github Gists
        • 112
          Github pages
        • 83
          Easy to find repos
        • 62
          Open source
        • 60
          It's free
        • 60
          Easy to find projects
        • 56
          Network effect
        • 49
          Extensive API
        • 43
          Organizations
        • 42
          Branching
        • 34
          Developer Profiles
        • 32
          Git Powered Wikis
        • 30
          Great for collaboration
        • 24
          It's fun
        • 23
          Clean interface and good integrations
        • 22
          Community SDK involvement
        • 20
          Learn from others source code
        • 16
          Because: Git
        • 14
          It integrates directly with Azure
        • 10
          Standard in Open Source collab
        • 10
          Newsfeed
        • 8
          It integrates directly with Hipchat
        • 8
          Fast
        • 8
          Beautiful user experience
        • 7
          Easy to discover new code libraries
        • 6
          Smooth integration
        • 6
          Cloud SCM
        • 6
          Nice API
        • 6
          Graphs
        • 6
          Integrations
        • 6
          It's awesome
        • 5
          Quick Onboarding
        • 5
          Reliable
        • 5
          Remarkable uptime
        • 5
          CI Integration
        • 5
          Hands down best online Git service available
        • 4
          Uses GIT
        • 4
          Version Control
        • 4
          Simple but powerful
        • 4
          Unlimited Public Repos at no cost
        • 4
          Free HTML hosting
        • 4
          Security options
        • 4
          Loved by developers
        • 4
          Easy to use and collaborate with others
        • 3
          Ci
        • 3
          IAM
        • 3
          Nice to use
        • 3
          Easy deployment via SSH
        • 2
          Easy to use
        • 2
          Leads the copycats
        • 2
          All in one development service
        • 2
          Free private repos
        • 2
          Free HTML hostings
        • 2
          Easy and efficient maintainance of the projects
        • 2
          Beautiful
        • 2
          Easy source control and everything is backed up
        • 2
          IAM integration
        • 2
          Very Easy to Use
        • 2
          Good tools support
        • 2
          Issues tracker
        • 2
          Never dethroned
        • 2
          Self Hosted
        • 1
          Dasf
        • 1
          Profound
        CONS OF GITHUB
        • 54
          Owned by micrcosoft
        • 38
          Expensive for lone developers that want private repos
        • 15
          Relatively slow product/feature release cadence
        • 10
          API scoping could be better
        • 9
          Only 3 collaborators for private repos
        • 4
          Limited featureset for issue management
        • 3
          Does not have a graph for showing history like git lens
        • 2
          GitHub Packages does not support SNAPSHOT versions
        • 1
          No multilingual interface
        • 1
          Takes a long time to commit
        • 1
          Expensive

        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

        See more
        Python logo

        Python

        241.8K
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        A clear and powerful object-oriented programming language, comparable to Perl, Ruby, Scheme, or Java.
        241.8K
        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
          Very programmer and non-programmer friendly
        • 18
          Free
        • 17
          Machine learning support
        • 17
          Powerfull language
        • 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
          Import antigravity
        • 8
          It's lean and fun to code
        • 7
          Python has great libraries for data processing
        • 7
          Print "life is short, use python"
        • 6
          Although practicality beats purity
        • 6
          Readability counts
        • 6
          Rapid Prototyping
        • 6
          Fast coding and good for competitions
        • 6
          There should be one-- and preferably only one --obvious
        • 6
          Now is better than never
        • 6
          High Documented language
        • 6
          I love snakes
        • 6
          Flat is better than nested
        • 6
          Great for tooling
        • 5
          Lists, tuples, dictionaries
        • 5
          Great for analytics
        • 4
          Beautiful is better than ugly
        • 4
          Multiple Inheritence
        • 4
          Socially engaged community
        • 4
          CG industry needs
        • 4
          Easy to learn and use
        • 4
          Simple and easy to learn
        • 4
          Easy to setup and run smooth
        • 4
          Complex is better than complicated
        • 4
          Web scraping
        • 4
          Plotting
        • 3
          No cruft
        • 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
        • 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
          Generators
        • 3
          Import this
        • 2
          Good for hacking
        • 2
          Flexible and easy
        • 2
          Batteries included
        • 2
          Can understand easily who are new to programming
        • 2
          Powerful language for AI
        • 2
          Should START with this but not STICK with This
        • 2
          A-to-Z
        • 2
          Because of Netflix
        • 2
          Only one way to do it
        • 2
          Better outcome
        • 1
          Automation friendly
        • 1
          Securit
        • 1
          Slow
        • 1
          Sexy af
        • 1
          Procedural programming
        • 0
          Powerful
        • 0
          Ni
        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

        See more
        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

        See more