Alternatives to Microsoft Azure logo

Alternatives to Microsoft Azure

Google Cloud Platform, DigitalOcean, OneDrive, Hadoop, and Oracle are the most popular alternatives and competitors to Microsoft Azure.
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What is Microsoft Azure and what are its top alternatives?

Microsoft Azure is a cloud computing platform offered by Microsoft that provides a wide range of services including virtual machines, storage, databases, analytics, and more. Key features of Azure include scalability, flexibility, security, and a global network of data centers. However, some limitations of Azure include complex pricing structure, potential performance issues, and a steeper learning curve for beginners.

  1. Amazon Web Services (AWS): AWS is one of the largest cloud computing platforms with a wide range of services, strong global presence, and flexible pricing options. Pros include a vast service catalog, strong security, and global reach. Cons include complex pricing and a steep learning curve.

  2. Google Cloud Platform (GCP): GCP offers services for computing, storage, machine learning, and more with strong focus on data analytics and AI. Pros include strong machine learning capabilities, flexible pricing, and global network. Cons include less services compared to AWS and Azure.

  3. IBM Cloud: IBM Cloud offers a range of cloud services including AI, blockchain, and Internet of Things (IoT) with a focus on enterprise solutions. Pros include strong security features, compliance-ready services, and hybrid cloud options. Cons include limited third-party services compared to AWS and Azure.

  4. Oracle Cloud Infrastructure (OCI): OCI provides compute, storage, and networking services along with database options with a focus on enterprise-grade performance and security. Pros include high performance computing options, strong database offerings, and hybrid cloud support. Cons include limited third-party integrations and smaller service catalog compared to AWS and Azure.

  5. Alibaba Cloud: Alibaba Cloud is a leading cloud provider in China offering a wide range of services including data storage, databases, and AI. Pros include strong presence in Asia, flexible pricing options, and global network. Cons include limited global reach compared to other cloud providers.

  6. DigitalOcean: DigitalOcean provides simple cloud infrastructure with services for virtual machines, databases, and Kubernetes. Pros include ease of use, predictable pricing, and strong community support. Cons include limited services compared to AWS and Azure.

  7. VMware Cloud: VMware Cloud offers a range of cloud services for virtualization, networking, and management with a focus on hybrid cloud solutions. Pros include seamless integration with existing VMware environments, enterprise-grade security, and multi-cloud management. Cons include limited support for non-VMware workloads and higher costs for some services.

  8. Red Hat OpenShift: OpenShift is a container platform based on Kubernetes offering container orchestration, automation, and developer tools. Pros include easy container deployment, strong security features, and extensive ecosystem of tools. Cons include complex setup for beginners and potential scalability challenges.

  9. Salesforce Heroku: Heroku is a cloud platform that enables developers to build, deliver, monitor, and scale applications quickly and efficiently. Pros include ease of use, streamlined development process, and strong support for multiple programming languages. Cons include limited scalability options for large applications.

  10. Hewlett Packard Enterprise (HPE) GreenLake: HPE GreenLake offers a range of cloud services including infrastructure, data storage, and workload management with a focus on pay-per-use pricing model. Pros include flexible consumption options, strong security features, and simplified IT operations. Cons include limited service catalog compared to major cloud providers.

Top Alternatives to Microsoft Azure

  • Google Cloud Platform
    Google Cloud Platform

    It helps you build what's next with secure infrastructure, developer tools, APIs, data analytics and machine learning. It is a suite of cloud computing services that runs on the same infrastructure that Google uses internally for its end-user products, such as Google Search and YouTube. ...

  • DigitalOcean
    DigitalOcean

    We take the complexities out of cloud hosting by offering blazing fast, on-demand SSD cloud servers, straightforward pricing, a simple API, and an easy-to-use control panel. ...

  • OneDrive
    OneDrive

    Outlook.com is a free, personal email service from Microsoft. Keep your inbox clutter-free with powerful organizational tools, and collaborate easily with OneDrive and Office Online integration. ...

  • Hadoop
    Hadoop

    The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. ...

  • Oracle
    Oracle

    Oracle Database is an RDBMS. An RDBMS that implements object-oriented features such as user-defined types, inheritance, and polymorphism is called an object-relational database management system (ORDBMS). Oracle Database has extended the relational model to an object-relational model, making it possible to store complex business models in a relational database. ...

  • 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. ...

Microsoft Azure alternatives & related posts

Google Cloud Platform logo

Google Cloud Platform

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A suite of cloud computing services
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PROS OF GOOGLE CLOUD PLATFORM
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    Good app Marketplace for Beginner and Advanced User
  • 4
    1 year free trial credit USD300
  • 3
    Premium tier IP address
  • 3
    Live chat support
  • 3
    Cheap
CONS OF GOOGLE CLOUD PLATFORM
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    related Google Cloud Platform posts

    My days of using Firebase are over! I want to move to something scalable and possibly less cheap. In the past seven days I have done my research on what type of DB best fits my needs, and have chosen to go with the nonrelational DB; MongoDB. Although I understand it, I need help understanding how to set up the architecture. I have the client app (Flutter/ Dart) that would make HTTP requests to the web server (node/express), and from there the webserver would query data from MongoDB.

    How should I go about hosting the web server and MongoDb; do they have to be hosted together (this is where a lot of my confusion is)? Based on the research I've done, it seems like the standard practice would be to host on a VM provided by services such as Amazon Web Services, Google Cloud Platform, Microsoft Azure, etc. If there are better ways, such as possibly self-hosting (more responsibility), should I? Anyways, I just want to confirm with a community (you guys) to make sure I do this right, all input is highly appreciated.

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    waheed khan
    Associate Java Developer at txtsol · | 8 upvotes · 31.3K views

    I want to make application like Zomato, #Foodpanda.

    Which stack is best for this? As I have expertise in Java and Angular. What is the best stack you will recommend?

    Web Micro-service / Mono? Angular / React? Amazon Web Services (AWS) / Google Cloud Platform? DB : SQL or No SQL

    Mob Cross-platform: React Native / Flutter

    Note: We are a team of 5. what languages do you recommend if I go with microservices?

    Thanks

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    DigitalOcean logo

    DigitalOcean

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    Deploy an SSD cloud server in less than 55 seconds with a dedicated IP and root access.
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    PROS OF DIGITALOCEAN
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      Great value for money
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      Simple dashboard
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      Good pricing
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      Ssds
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      Nice ui
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      Easy configuration
    • 156
      Great documentation
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      Ssh access
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      Great community
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      Ubuntu
    • 13
      Docker
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      IPv6 support
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      Private networking
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      99.99% uptime SLA
    • 7
      Simple API
    • 7
      Great tutorials
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      55 Second Provisioning
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      One Click Applications
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      Dokku
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      Node.js
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      LAMP
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      Debian
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      CoreOS
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      1Gb/sec Servers
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      Word Press
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      LEMP
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      Simple Control Panel
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      Mean
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      Ghost
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      Runs CoreOS
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      Quick and no nonsense service
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      Django
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      Good Tutorials
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      Speed
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      Ruby on Rails
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      GitLab
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      Hex Core machines with dedicated ECC Ram and RAID SSD s
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      CentOS
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      Spaces
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      KVM Virtualization
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      Amazing Hardware
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      Transfer Globally
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      Fedora
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      FreeBSD
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      Drupal
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      FreeBSD Amp
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      Magento
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      ownCloud
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      RedMine
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      My go to server provider
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      Ease and simplicity
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      Nice
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      Find it superfitting with my requirements (SSD, ssh.
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      Easy Setup
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      Cheap
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      Static IP
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      It's the easiest to get started for small projects
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      Automatic Backup
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      Great support
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      Quick and easy to set up
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      Servers on demand - literally
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      Reliability
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      Variety of services
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      Managed Kubernetes
    CONS OF DIGITALOCEAN
    • 3
      No live support chat
    • 3
      Pricing

    related DigitalOcean posts

    David Watson
    at Realtime App Solutions · | 15 upvotes · 98.8K views

    Coming from a non-web development environment background, I was a bit lost a first and bewildered by all the varying tools and platforms, and spent much too long evaluating before eventualy deciding on Laravel as the main core of my development.

    But as I started development with Laravel that lead me into discovering Vue.js for creating beautiful front-end components that were easy to configure and extend, so I decided to standardise on Vue.js for most of my front-end development.

    During my search for additional Vue.js components, a chance comment in a @laravel forum , led me to discover Quasar Framework initially for it's wide range of in-built components ... but once, I realised that Quasar Framework allowed me to use the same codebase to create apps for SPA, PWA, iOS, Android, and Electron then I was hooked.

    So, I'm now using mainly just Quasar Framework for all the front-end, with Laravel providing a backend API service to the Front-end apps.

    I'm deploying this all to DigitalOcean droplets via service called Moss.sh which deploys my private GitHub repositories directly to DigitalOcean in realtime.

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    Christopher Wray
    Web Developer at Soltech LLC · | 14 upvotes · 172.3K views

    This week, we finally released NurseryPeople.com. In the end, I chose to provision our server on DigitalOcean. So far, I am SO happy with that decision. Although setting everything up was a challenge, and I learned a lot, DigitalOceans blogs helped in so many ways. I was able to set up nginx and the Laravel web app pretty smoothly. I am also using Buddy for deploying changes made in git, which is super awesome. All I have to do in order to deploy is push my code to my private repo, and buddy transfers everything over to DigitalOcean. So far, we haven't had any downtime and DigitalOceans prices are quite fair for the power under the hood.

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    OneDrive logo

    OneDrive

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    Save your files and photos to OneDrive and get them from any device, anywhere
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    PROS OF ONEDRIVE
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      FREE
    • 2
      Simple
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      Back up
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      Stable service
    CONS OF ONEDRIVE
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      related OneDrive posts

      Hadoop logo

      Hadoop

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      Open-source software for reliable, scalable, distributed computing
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      PROS OF HADOOP
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        Great ecosystem
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        One stack to rule them all
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        Great load balancer
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        Amazon aws
      • 1
        Java syntax
      CONS OF HADOOP
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        related Hadoop posts

        Shared insights
        on
        KafkaKafkaHadoopHadoop
        at

        The early data ingestion pipeline at Pinterest used Kafka as the central message transporter, with the app servers writing messages directly to Kafka, which then uploaded log files to S3.

        For databases, a custom Hadoop streamer pulled database data and wrote it to S3.

        Challenges cited for this infrastructure included high operational overhead, as well as potential data loss occurring when Kafka broker outages led to an overflow of in-memory message buffering.

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        Conor Myhrvold
        Tech Brand Mgr, Office of CTO at Uber · | 7 upvotes · 3M views

        Why we built Marmaray, an open source generic data ingestion and dispersal framework and library for Apache Hadoop :

        Built and designed by our Hadoop Platform team, Marmaray is a plug-in-based framework built on top of the Hadoop ecosystem. Users can add support to ingest data from any source and disperse to any sink leveraging the use of Apache Spark . The name, Marmaray, comes from a tunnel in Turkey connecting Europe and Asia. Similarly, we envisioned Marmaray within Uber as a pipeline connecting data from any source to any sink depending on customer preference:

        https://eng.uber.com/marmaray-hadoop-ingestion-open-source/

        (Direct GitHub repo: https://github.com/uber/marmaray Kafka Kafka Manager )

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        Oracle logo

        Oracle

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        An RDBMS that implements object-oriented features such as user-defined types, inheritance, and polymorphism
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        PROS OF ORACLE
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          Reliable
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          Enterprise
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          Expensive
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          Hard to maintain
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          Maintainable
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          Hard to use
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          High complexity
        CONS OF ORACLE
        • 14
          Expensive

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        Saurav Pandit
        Application Devloper at Bny Mellon · | 9 upvotes · 258.6K views

        I have just started learning Python 3 week back. I want to create REST api using python. The api will be use to save form data in Oracle database. The front end is using AngularJS 8 with Angular Material. In python there are so many framework for developing REST ** I am looking for some suggestions which REST framework to choose? ** Here are some feature I am looking for * Easy integration and unit testing like in Angular we just run command. * Code packageing, like in Java maven project we can build and package. I am looking for something which I can push in artifactory and deploy whole code as package. *Support for swagger/ OpenAPI * Support for JSON Web Token * Support for testcase coverage report Framework can have feature included or can be available by extension.

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        Dishi Jain
        Shared insights
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        OracleOracleKubernetesKubernetes

        So we are re-engineering our application database to make it cloud-native and deploy on the Kubernetes platform. Currently, our data lies on the Oracle 19c database and it is normalized extensively. We store pdfs, txt files and allow a user to edit, delete, view, create new transactions. Now I want to pick a DB, which makes the re-engineering, not a big deal but allows us to store data in a distributed manner on Kubernetes. Please assist me.

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        JavaScript logo

        JavaScript

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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
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          Ubiquitousness
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          Expressive
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          Extended functionality to web pages
        • 49
          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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          Pure Javascript
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          Functional programming
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          Async
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          Full-stack
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          Setup is easy
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          Its everywhere
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          Future Language of The Web
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          JavaScript is the New PHP
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          Because I love functions
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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
        • 9
          Can be used in backend, frontend and DB
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          Easy
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          Easy to hire developers
        • 8
          No need to use PHP
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          For the good parts
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          Can be used both as frontend and backend as well
        • 8
          Powerful
        • 8
          Most Popular Language in the World
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          Popularized Class-Less Architecture & Lambdas
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          It's fun
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          Nice
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          Versitile
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          Hard not to use
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          Its fun and fast
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          Agile, packages simple to use
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          Supports lambdas and closures
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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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          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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          It let's me use Babel & Typescript
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          Easy to make something
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          Can be used on frontend/backend/Mobile/create PRO Ui
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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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          Clojurescript
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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
          Test2
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          Easy to learn
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          Easy to understand
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          Not the best
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          Hard to learn
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          Subskill #4
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          Test
        • 0
          Hard 彤
        CONS OF JAVASCRIPT
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          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

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        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 · 10.1M 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
        • 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
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          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

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        Simon Reymann
        Senior Fullstack Developer at QUANTUSflow Software GmbH · | 30 upvotes · 9.2M 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 · 8.3M 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.

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