Alternatives to Apache CloudStack logo

Alternatives to Apache CloudStack

OpenStack, Kubernetes, OpenNebula, JavaScript, and Git are the most popular alternatives and competitors to Apache CloudStack.
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What is Apache CloudStack and what are its top alternatives?

Apache CloudStack is an open-source cloud management platform that helps organizations deploy and manage large networks of virtual machines. Its key features include support for multiple hypervisors, a user-friendly web interface, automated provisioning, and scalability. However, some limitations of Apache CloudStack include a steep learning curve for beginners, complex installation process, and limited community support.

  1. OpenStack: OpenStack is a popular open-source cloud computing platform that offers scalability, flexibility, and modularity. Its key features include support for multiple hypervisors, comprehensive networking options, and a large community for support. However, OpenStack can be complex to deploy and manage for small organizations.

  2. Proxmox Virtual Environment: Proxmox is an open-source virtualization platform that combines KVM and LXC containers. Its key features include easy deployment, high availability, live migration, and a user-friendly web interface. However, Proxmox lacks some advanced features compared to Apache CloudStack.

  3. OpenNebula: OpenNebula is an open-source cloud management platform that offers simple deployment, flexibility, and compatibility with multiple hypervisors. Its key features include a self-service portal, integration with existing data centers, and support for hybrid cloud environments. However, OpenNebula may lack some advanced networking features compared to Apache CloudStack.

  4. VMware vCloud Director: VMware vCloud Director is a cloud management platform that offers multi-tenancy, scalability, and resource management. Its key features include support for VMware-based environments, self-service provisioning, and automation. However, VMware vCloud Director may be more expensive than Apache CloudStack for some organizations.

  5. Microsoft Azure Stack: Microsoft Azure Stack is an extension of Azure that enables organizations to build and deploy hybrid cloud environments. Its key features include consistent hybrid cloud management, deployment flexibility, and integration with Azure services. However, Microsoft Azure Stack may be more suitable for organizations already using the Azure ecosystem.

  6. Google Cloud Platform (GCP): Google Cloud Platform is a suite of cloud computing services offered by Google. Its key features include global infrastructure, machine learning capabilities, and a wide range of storage options. However, GCP may not provide the same level of customization and control as Apache CloudStack.

  7. Dell Cloud Manager: Dell Cloud Manager is a cloud management platform that offers automation, governance, and cost control. Its key features include multi-cloud management, application deployment, and policy-based resources. However, Dell Cloud Manager may be more suitable for organizations with Dell infrastructure.

  8. Nutanix: Nutanix is a hyper-converged infrastructure platform that combines storage, compute, and networking. Its key features include scalability, simplicity, and automation. However, Nutanix may not offer the same level of cloud management capabilities as Apache CloudStack.

  9. Citrix CloudPlatform: Citrix CloudPlatform is a cloud orchestration platform that offers self-service provisioning, automation, and scalability. Its key features include support for multiple hypervisors, integration with Citrix products, and flexibility. However, Citrix CloudPlatform may be more suitable for organizations using Citrix technology stack.

  10. Alibaba Cloud: Alibaba Cloud is a cloud computing platform offered by Alibaba Group. Its key features include global infrastructure, security, and scalability. However, Alibaba Cloud may not offer the same level of customization and control as Apache CloudStack.

Top Alternatives to Apache CloudStack

  • OpenStack
    OpenStack

    OpenStack is a cloud operating system that controls large pools of compute, storage, and networking resources throughout a datacenter, all managed through a dashboard that gives administrators control while empowering their users to provision resources through a web interface. ...

  • Kubernetes
    Kubernetes

    Kubernetes is an open source orchestration system for Docker containers. It handles scheduling onto nodes in a compute cluster and actively manages workloads to ensure that their state matches the users declared intentions. ...

  • OpenNebula
    OpenNebula

    It provides a simple but feature-rich and flexible solution for the comprehensive management of virtualized data centers to enable on-premise enterprise clouds in existing infrastructures. It can be primarily used as a virtualization tool to manage your virtual infrastructure in the data-center or cluster, which is usually referred as Private Cloud. It supports Hybrid Cloud to combine local infrastructure with public cloud-based infrastructure, enabling highly scalable hosting environments. ...

  • 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. ...

Apache CloudStack alternatives & related posts

OpenStack logo

OpenStack

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1.1K
131
Open source software for building private and public clouds
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PROS OF OPENSTACK
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    Private cloud
  • 38
    Avoid vendor lock-in
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  • 6
    Industry leader
  • 4
    Supported by many companies in top500
  • 4
    Robust architecture
CONS OF OPENSTACK
    Be the first to leave a con

    related OpenStack posts

    Shared insights
    on
    UbuntuUbuntuOpenStackOpenStackCentOSCentOS
    at

    Hello guys

    I am confused between choosing CentOS7 or centos8 for OpenStack tripleo undercloud deployment. Which one should I use? There is another option to use OpenStack, Ubuntu, or MicroStack.

    We wanted to use this deployment to build our home cloud or private cloud infrastructure. I heard that centOS is always the best choice through a little research, but still not sure. As centos8 from Redhat is not supported for OpenStack tripleo deployments anymore, I had to upgrade to CentosStream.

    See more
    Kubernetes logo

    Kubernetes

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    678
    Manage a cluster of Linux containers as a single system to accelerate Dev and simplify Ops
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    • 58
      The right abstractions
    • 25
      Scale services
    • 20
      Replication controller
    • 11
      Permission managment
    • 9
      Supports autoscaling
    • 8
      Cheap
    • 8
      Simple
    • 6
      Self-healing
    • 5
      Promotes modern/good infrascture practice
    • 5
      Open, powerful, stable
    • 5
      Reliable
    • 5
      No cloud platform lock-in
    • 4
      Scalable
    • 4
      Quick cloud setup
    • 3
      Custom and extensibility
    • 3
      A self healing environment with rich metadata
    • 3
      Cloud Agnostic
    • 3
      Backed by Red Hat
    • 3
      Runs on azure
    • 3
      Captain of Container Ship
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      Expandable
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      Sfg
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      Everything of CaaS
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      Golang
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    CONS OF KUBERNETES
    • 16
      Steep learning curve
    • 15
      Poor workflow for development
    • 8
      Orchestrates only infrastructure
    • 4
      High resource requirements for on-prem clusters
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      Additional vendor lock-in (Docker)
    • 1
      More moving parts to secure
    • 1
      Additional Technology Overhead

    related Kubernetes 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
    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

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    OpenNebula logo

    OpenNebula

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    0
    A cloud computing platform for managing heterogeneous distributed data center infrastructures
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        related OpenNebula posts

        JavaScript logo

        JavaScript

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          Ubiquitousness
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          Relatively easy language
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          Relatively fast to the end user
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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
        • 10
          Like it or not, JS is part of the web standard
        • 9
          Everyone use it
        • 9
          Expansive community
        • 9
          Easy
        • 9
          Can be used in backend, frontend and DB
        • 8
          Easy to hire developers
        • 8
          No need to use PHP
        • 8
          For the good parts
        • 8
          Can be used both as frontend and backend as well
        • 8
          Powerful
        • 8
          Most Popular Language in the World
        • 7
          Evolution of C
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          Hard not to use
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          Versitile
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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
        • 7
          Nice
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          It's fun
        • 7
          Popularized Class-Less Architecture & Lambdas
        • 7
          Agile, packages simple to use
        • 6
          Can be used on frontend/backend/Mobile/create PRO Ui
        • 6
          1.6K Can be used on frontend/backend
        • 6
          Client side JS uses the visitors CPU to save Server Res
        • 6
          It let's me use Babel & Typescript
        • 6
          Easy to make something
        • 5
          Client processing
        • 5
          Everywhere
        • 5
          Scope manipulation
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          Function expressions are useful for callbacks
        • 5
          Stockholm Syndrome
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          Promise relationship
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          Clojurescript
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          What to add
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          Only Programming language on browser
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          Because it is so simple and lightweight
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          Easy to understand
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          Test
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          Test2
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          Subskill #4
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          Easy to learn
        • 1
          Hard to learn
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          Not the best
        • 0
          Hard 彤
        CONS OF JAVASCRIPT
        • 22
          A constant moving target, too much churn
        • 20
          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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        Fast, scalable, distributed revision control system
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        PROS OF GIT
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          Fast
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          Open source
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          Better than svn
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          Great command-line application
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          Simple
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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
        • 15
          Staging Area
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          Disposable Experimentation
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          Data Assurance
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          Efficient
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          Just awesome
        • 3
          Github integration
        • 3
          Easy branching and merging
        • 2
          Compatible
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          Flexible
        • 2
          Possible to lose history and commits
        • 1
          Rebase supported natively; reflog; access to plumbing
        • 1
          Light
        • 1
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        • 1
          Fast, scalable, distributed revision control system
        • 1
          Easy
        • 1
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        • 1
          CLI is great, but the GUI tools are awesome
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          It's what you do
        • 0
          Phinx
        CONS OF GIT
        • 16
          Hard to learn
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          Inconsistent command line interface
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          Easy to lose uncommitted work
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          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 · 10M 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.

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        GitHub logo

        GitHub

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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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          Good tools support
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          Issues tracker
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        • 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

        241.9K
        197.2K
        6.9K
        A clear and powerful object-oriented programming language, comparable to Perl, Ruby, Scheme, or Java.
        241.9K
        197.2K
        + 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
        jQuery logo

        jQuery

        190.8K
        67.3K
        6.6K
        The Write Less, Do More, JavaScript Library.
        190.8K
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        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

        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