Alternatives to Fabric logo

Alternatives to Fabric

Ansible, Azure Service Fabric, Kubernetes, Liquid, and Forge are the most popular alternatives and competitors to Fabric.
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What is Fabric and what are its top alternatives?

Fabric is a Python (2.5-2.7) library and command-line tool for streamlining the use of SSH for application deployment or systems administration tasks. It provides a basic suite of operations for executing local or remote shell commands (normally or via sudo) and uploading/downloading files, as well as auxiliary functionality such as prompting the running user for input, or aborting execution.
Fabric is a tool in the Server Configuration and Automation category of a tech stack.
Fabric is an open source tool with 13.1K GitHub stars and 1.8K GitHub forks. Here’s a link to Fabric's open source repository on GitHub

Top Alternatives to Fabric

  • Ansible

    Ansible

    Ansible is an IT automation tool. It can configure systems, deploy software, and orchestrate more advanced IT tasks such as continuous deployments or zero downtime rolling updates. Ansible’s goals are foremost those of simplicity and maximum ease of use. ...

  • Azure Service Fabric

    Azure Service Fabric

    Azure Service Fabric is a distributed systems platform that makes it easy to package, deploy, and manage scalable and reliable microservices. Service Fabric addresses the significant challenges in developing and managing cloud apps. ...

  • 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. ...

  • Liquid

    Liquid

    It is an open-source template language written in Ruby. It is the backbone of Shopify themes and is used to load dynamic content on storefronts. It is safe, customer facing template language for flexible web apps. ...

  • Forge

    Forge

    Fastest possible way to host lighting-fast static websites for small businesses, web startups, and app developers. ...

  • Material

    Material

    Express your creativity with Material, an animation and graphics framework for Google's Material Design and Apple's Flat UI in Swift. ...

  • Fiber

    Fiber

    Fiber UI Kit is the perfect starting place for your next project. Each element has been designed to work independently or as one seamless flow. It’s a full-fledged prototype with customizable components. ...

  • Chef

    Chef

    Chef enables you to manage and scale cloud infrastructure with no downtime or interruptions. Freely move applications and configurations from one cloud to another. Chef is integrated with all major cloud providers including Amazon EC2, VMWare, IBM Smartcloud, Rackspace, OpenStack, Windows Azure, HP Cloud, Google Compute Engine, Joyent Cloud and others. ...

Fabric alternatives & related posts

Ansible logo

Ansible

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Radically simple configuration-management, application deployment, task-execution, and multi-node orchestration engine
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PROS OF ANSIBLE
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    Agentless
  • 204
    Great configuration
  • 195
    Simple
  • 173
    Powerful
  • 151
    Easy to learn
  • 66
    Flexible
  • 54
    Doesn't get in the way of getting s--- done
  • 34
    Makes sense
  • 29
    Super efficient and flexible
  • 27
    Powerful
  • 11
    Dynamic Inventory
  • 8
    Backed by Red Hat
  • 7
    Works with AWS
  • 6
    Cloud Oriented
  • 6
    Easy to maintain
  • 4
    Because SSH
  • 4
    Multi language
  • 4
    Easy
  • 4
    Simple
  • 4
    Procedural or declarative, or both
  • 4
    Simple and powerful
  • 3
    Consistency
  • 3
    Vagrant provisioner
  • 2
    Fast as hell
  • 2
    Masterless
  • 2
    Well-documented
  • 2
    Merge hash to get final configuration similar to hiera
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    Debugging is simple
  • 1
    Work on windows, but difficult to manage
  • 1
    Certified Content
CONS OF ANSIBLE
  • 5
    Dangerous
  • 5
    Hard to install
  • 3
    Bloated
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    Backward compatibility
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    Doesn't Run on Windows
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    No immutable infrastructure

related Ansible posts

Tymoteusz Paul
Devops guy at X20X Development LTD · | 23 upvotes · 4.6M 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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Sebastian Gębski

Heroku was a decent choice to start a business, but at some point our platform was too big, too complex & too heterogenic, so Heroku started to be a constraint, not a benefit. First, we've started containerizing our apps with Docker to eliminate "works in my machine" syndrome & uniformize the environment setup. The first orchestration was composed with Docker Compose , but at some point it made sense to move it to Kubernetes. Fortunately, we've made a very good technical decision when starting our work with containers - all the container configuration & provisions HAD (since the beginning) to be done in code (Infrastructure as Code) - we've used Terraform & Ansible for that (correspondingly). This general trend of containerisation was accompanied by another, parallel & equally big project: migrating environments from Heroku to AWS: using Amazon EC2 , Amazon EKS, Amazon S3 & Amazon RDS.

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Azure Service Fabric logo

Azure Service Fabric

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Distributed systems platform that simplifies build, package, deploy, and management of scalable microservices apps
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PROS OF AZURE SERVICE FABRIC
  • 5
    Intelligent, fast, reliable
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    Open source
  • 3
    Superior programming models
  • 3
    More reliable than Kubernetes
  • 3
    Runs most of Azure core services
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    Reliability
  • 2
    Quickest recovery and healing in the world
  • 1
    Deploy anywhere
  • 1
    Is data storage technology
  • 1
    Battle hardened in Azure > 10 Years
CONS OF AZURE SERVICE FABRIC
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    related Azure Service Fabric posts

    Kubernetes logo

    Kubernetes

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    Manage a cluster of Linux containers as a single system to accelerate Dev and simplify Ops
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    PROS OF KUBERNETES
    • 159
      Leading docker container management solution
    • 124
      Simple and powerful
    • 101
      Open source
    • 75
      Backed by google
    • 56
      The right abstractions
    • 24
      Scale services
    • 18
      Replication controller
    • 9
      Permission managment
    • 7
      Simple
    • 7
      Supports autoscaling
    • 6
      Cheap
    • 4
      Self-healing
    • 4
      Reliable
    • 4
      No cloud platform lock-in
    • 3
      Open, powerful, stable
    • 3
      Scalable
    • 3
      Quick cloud setup
    • 3
      Promotes modern/good infrascture practice
    • 2
      Backed by Red Hat
    • 2
      Runs on azure
    • 2
      Cloud Agnostic
    • 2
      Custom and extensibility
    • 2
      Captain of Container Ship
    • 2
      A self healing environment with rich metadata
    • 1
      Golang
    • 1
      Easy setup
    • 1
      Everything of CaaS
    • 1
      Sfg
    • 1
      Expandable
    • 1
      Gke
    CONS OF KUBERNETES
    • 13
      Poor workflow for development
    • 11
      Steep learning curve
    • 5
      Orchestrates only infrastructure
    • 2
      High resource requirements for on-prem clusters

    related Kubernetes posts

    Conor Myhrvold
    Tech Brand Mgr, Office of CTO at Uber · | 39 upvotes · 4.2M views

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

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

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

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

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

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

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    Yshay Yaacobi

    Our first experience with .NET core was when we developed our OSS feature management platform - Tweek (https://github.com/soluto/tweek). We wanted to create a solution that is able to run anywhere (super important for OSS), has excellent performance characteristics and can fit in a multi-container architecture. We decided to implement our rule engine processor in F# , our main service was implemented in C# and other components were built using JavaScript / TypeScript and Go.

    Visual Studio Code worked really well for us as well, it worked well with all our polyglot services and the .Net core integration had great cross-platform developer experience (to be fair, F# was a bit trickier) - actually, each of our team members used a different OS (Ubuntu, macos, windows). Our production deployment ran for a time on Docker Swarm until we've decided to adopt Kubernetes with almost seamless migration process.

    After our positive experience of running .Net core workloads in containers and developing Tweek's .Net services on non-windows machines, C# had gained back some of its popularity (originally lost to Node.js), and other teams have been using it for developing microservices, k8s sidecars (like https://github.com/Soluto/airbag), cli tools, serverless functions and other projects...

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    Liquid logo

    Liquid

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    Open-source template language written in Ruby
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    PROS OF LIQUID
      Be the first to leave a pro
      CONS OF LIQUID
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        related Liquid posts

        Forge logo

        Forge

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        Static web hosting made simple
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        PROS OF FORGE
        • 1
          Fgfgf
        CONS OF FORGE
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          related Forge posts

          Material logo

          Material

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          A Graphics Framework for Material Design in Swift
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          PROS OF MATERIAL
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            CONS OF MATERIAL
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              related Material posts

              Fiber logo

              Fiber

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              An interactive UI Kit by Framer.
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              PROS OF FIBER
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                CONS OF FIBER
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                  related Fiber posts

                  Chef logo

                  Chef

                  1.1K
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                  Build, destroy and rebuild servers on any public or private cloud
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                  PROS OF CHEF
                  • 109
                    Dynamic and idempotent server configuration
                  • 76
                    Reusable components
                  • 47
                    Integration testing with Vagrant
                  • 43
                    Repeatable
                  • 30
                    Mock testing with Chefspec
                  • 14
                    Ruby
                  • 8
                    Can package cookbooks to guarantee repeatability
                  • 7
                    Works with AWS
                  • 3
                    Has marketplace where you get readymade cookbooks
                  • 3
                    Matured product with good community support
                  • 2
                    Less declarative more procedural
                  • 2
                    Open source configuration mgmt made easy(ish)
                  CONS OF CHEF
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                    related Chef posts

                    In late 2013, the Operations Engineering team at PagerDuty was made up of 4 engineers, and was comprised of generalists, each of whom had one or two areas of depth. Although the Operations Team ran its own on-call, each engineering team at PagerDuty also participated on the pager.

                    The Operations Engineering Team owned 150+ servers spanning multiple cloud providers, and used Chef to automate their infrastructure across the various cloud providers with a mix of completely custom cookbooks and customized community cookbooks.

                    Custom cookbooks were managed by Berkshelf, andach custom cookbook contained its own tests based on ChefSpec 3, coupled with Rspec.

                    Jenkins was used to GitHub for new changes and to handle unit testing of those features.

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                    Marcel Kornegoor

                    Since #ATComputing is a vendor independent Linux and open source specialist, we do not have a favorite Linux distribution. We mainly use Ubuntu , Centos Debian , Red Hat Enterprise Linux and Fedora during our daily work. These are also the distributions we see most often used in our customers environments.

                    For our #ci/cd training, we use an open source pipeline that is build around Visual Studio Code , Jenkins , VirtualBox , GitHub , Docker Kubernetes and Google Compute Engine.

                    For #ServerConfigurationAndAutomation, we have embraced and contributed to Ansible mainly because it is not only flexible and powerful, but also straightforward and easier to learn than some other (open source) solutions. On the other hand: we are not affraid of Puppet Labs and Chef either.

                    Currently, our most popular #programming #Language course is Python . The reason Python is so popular has to do with it's versatility, but also with its low complexity. This helps sysadmins to write scripts or simple programs to make their job less repetitive and automating things more fun. Python is also widely used to communicate with (REST) API's and for data analysis.

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