Alternatives to StackStorm logo

Alternatives to StackStorm

Ansible, Rundeck, Airflow, Jenkins, and Terraform are the most popular alternatives and competitors to StackStorm.
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What is StackStorm and what are its top alternatives?

StackStorm is a platform for integration and automation across services and tools. It ties together your existing infrastructure and application environment so you can more easily automate that environment -- with a particular focus on taking actions in response to events.
StackStorm is a tool in the Remote Server Task Execution category of a tech stack.
StackStorm is an open source tool with 4.2K GitHub stars and 552 GitHub forks. Here’s a link to StackStorm's open source repository on GitHub

Top Alternatives to StackStorm

  • 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. ...

  • Rundeck

    Rundeck

    A self-service operations platform used for support tasks, enterprise job scheduling, deployment, and more. ...

  • Airflow

    Airflow

    Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command lines utilities makes performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress and troubleshoot issues when needed. ...

  • Jenkins

    Jenkins

    In a nutshell Jenkins CI is the leading open-source continuous integration server. Built with Java, it provides over 300 plugins to support building and testing virtually any project. ...

  • Terraform

    Terraform

    With Terraform, you describe your complete infrastructure as code, even as it spans multiple service providers. Your servers may come from AWS, your DNS may come from CloudFlare, and your database may come from Heroku. Terraform will build all these resources across all these providers in parallel. ...

  • Salt

    Salt

    Salt is a new approach to infrastructure management. Easy enough to get running in minutes, scalable enough to manage tens of thousands of servers, and fast enough to communicate with them in seconds. Salt delivers a dynamic communication bus for infrastructures that can be used for orchestration, remote execution, configuration management and much more. ...

  • Neptune

    Neptune

    It brings organization and collaboration to data science projects. All the experiement-related objects are backed-up and organized ready to be analyzed, reproduced and shared with others. Works with all common technologies and integrates with other tools. ...

  • Neptune.io

    Neptune.io

    Neptune.io is a SaaS platform to automate your incident response. It integrates with your monitoring and alerting tools like NewRelic, Nagios, Pagerduty, CloudWatch etc. and lets you automate the remediation easily and much more. ...

StackStorm alternatives & related posts

Ansible logo

Ansible

12K
9.3K
1.3K
Radically simple configuration-management, application deployment, task-execution, and multi-node orchestration engine
12K
9.3K
+ 1
1.3K
PROS OF ANSIBLE
  • 272
    Agentless
  • 203
    Great configuration
  • 191
    Simple
  • 172
    Powerful
  • 149
    Easy to learn
  • 66
    Flexible
  • 54
    Doesn't get in the way of getting s--- done
  • 33
    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
    Procedural or declarative, or both
  • 4
    Simple and powerful
  • 4
    Easy
  • 4
    Simple
  • 4
    Because SSH
  • 4
    Multi language
  • 3
    Consistency
  • 3
    Vagrant provisioner
  • 2
    Masterless
  • 2
    Merge hash to get final configuration similar to hiera
  • 2
    Fast as hell
  • 2
    Well-documented
  • 2
    Debugging is simple
  • 1
    Work on windows, but difficult to manage
CONS OF ANSIBLE
  • 5
    Hard to install
  • 4
    Dangerous
  • 3
    Bloated
  • 3
    Backward compatibility
  • 2
    Doesn't Run on Windows
  • 2
    No immutable infrastructure

related Ansible posts

Tymoteusz Paul
Devops guy at X20X Development LTD · | 21 upvotes · 4M views

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

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

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

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

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

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

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

See more
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.

See more
Rundeck logo

Rundeck

140
212
6
A platform for Self-Service Operations
140
212
+ 1
6
PROS OF RUNDECK
  • 3
    Easy to understand
  • 3
    Role based access control
CONS OF RUNDECK
    Be the first to leave a con

    related Rundeck posts

    Airflow logo

    Airflow

    986
    1.7K
    100
    A platform to programmaticaly author, schedule and monitor data pipelines, by Airbnb
    986
    1.7K
    + 1
    100
    PROS OF AIRFLOW
    • 39
      Features
    • 12
      Task Dependency Management
    • 11
      Beautiful UI
    • 9
      Cluster of workers
    • 9
      Extensibility
    • 5
      Open source
    • 4
      Python
    • 3
      Complex workflows
    • 2
      K
    • 2
      Dashboard
    • 2
      Custom operators
    • 1
      Good api
    • 1
      Apache project
    CONS OF AIRFLOW
      Be the first to leave a con

      related Airflow posts

      Shared insights
      on
      JenkinsJenkinsAirflowAirflow

      I am looking for an open-source scheduler tool with cross-functional application dependencies. Some of the tasks I am looking to schedule are as follows:

      1. Trigger Matillion ETL loads
      2. Trigger Attunity Replication tasks that have downstream ETL loads
      3. Trigger Golden gate Replication Tasks
      4. Shell scripts, wrappers, file watchers
      5. Event-driven schedules

      I have used Airflow in the past, and I know we need to create DAGs for each pipeline. I am not familiar with Jenkins, but I know it works with configuration without much underlying code. I want to evaluate both and appreciate any advise

      See more

      I am looking for the best tool to orchestrate #ETL workflows in non-Hadoop environments, mainly for regression testing use cases. Would Airflow or Apache NiFi be a good fit for this purpose?

      For example, I want to run an Informatica ETL job and then run an SQL task as a dependency, followed by another task from Jira. What tool is best suited to set up such a pipeline?

      See more
      Jenkins logo

      Jenkins

      36.4K
      29.1K
      2.2K
      An extendable open source continuous integration server
      36.4K
      29.1K
      + 1
      2.2K
      PROS OF JENKINS
      • 520
        Hosted internally
      • 463
        Free open source
      • 313
        Great to build, deploy or launch anything async
      • 243
        Tons of integrations
      • 208
        Rich set of plugins with good documentation
      • 108
        Has support for build pipelines
      • 71
        Open source and tons of integrations
      • 63
        Easy setup
      • 61
        It is open-source
      • 54
        Workflow plugin
      • 11
        Configuration as code
      • 10
        Very powerful tool
      • 9
        Many Plugins
      • 8
        Git and Maven integration is better
      • 8
        Great flexibility
      • 6
        Continuous Integration
      • 6
        Slack Integration (plugin)
      • 6
        Github integration
      • 5
        Easy customisation
      • 5
        Self-hosted GitLab Integration (plugin)
      • 4
        100% free and open source
      • 4
        Docker support
      • 3
        Excellent docker integration
      • 3
        Fast builds
      • 3
        Platform idnependency
      • 2
        Pipeline API
      • 2
        Customizable
      • 2
        Can be run as a Docker container
      • 2
        It`w worked
      • 2
        Hosted Externally
      • 2
        AWS Integration
      • 2
        JOBDSL
      • 2
        It's Everywhere
      • 1
        NodeJS Support
      • 1
        PHP Support
      • 1
        Ruby/Rails Support
      • 1
        Universal controller
      • 1
        Easily extendable with seamless integration
      • 1
        Build PR Branch Only
      CONS OF JENKINS
      • 12
        Workarounds needed for basic requirements
      • 7
        Groovy with cumbersome syntax
      • 6
        Plugins compatibility issues
      • 6
        Limited abilities with declarative pipelines
      • 5
        Lack of support
      • 4
        No YAML syntax
      • 2
        Too tied to plugins versions

      related Jenkins posts

      Thierry Schellenbach

      Releasing new versions of our services is done by Travis CI. Travis first runs our test suite. Once it passes, it publishes a new release binary to GitHub.

      Common tasks such as installing dependencies for the Go project, or building a binary are automated using plain old Makefiles. (We know, crazy old school, right?) Our binaries are compressed using UPX.

      Travis has come a long way over the past years. I used to prefer Jenkins in some cases since it was easier to debug broken builds. With the addition of the aptly named “debug build” button, Travis is now the clear winner. It’s easy to use and free for open source, with no need to maintain anything.

      #ContinuousIntegration #CodeCollaborationVersionControl

      See more
      Tymoteusz Paul
      Devops guy at X20X Development LTD · | 21 upvotes · 4M views

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

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

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

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

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

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

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

      See more
      Terraform logo

      Terraform

      8.3K
      6.3K
      294
      Describe your complete infrastructure as code and build resources across providers
      8.3K
      6.3K
      + 1
      294
      PROS OF TERRAFORM
      • 101
        Infrastructure as code
      • 68
        Declarative syntax
      • 43
        Planning
      • 26
        Simple
      • 23
        Parallelism
      • 6
        Cloud agnostic
      • 5
        It's like coding your infrastructure in simple English
      • 4
        Well-documented
      • 3
        Automates infrastructure deployments
      • 3
        Platform agnostic
      • 3
        Immutable infrastructure
      • 2
        Automation
      • 2
        Portability
      • 2
        Scales to hundreds of hosts
      • 2
        Extendable
      • 1
        Lightweight
      CONS OF TERRAFORM
      • 1
        Doesn't have full support to GKE

      related Terraform posts

      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
      Praveen Mooli
      Engineering Manager at Taylor and Francis · | 14 upvotes · 1.6M views

      We are in the process of building a modern content platform to deliver our content through various channels. We decided to go with Microservices architecture as we wanted scale. Microservice architecture style is an approach to developing an application as a suite of small independently deployable services built around specific business capabilities. You can gain modularity, extensive parallelism and cost-effective scaling by deploying services across many distributed servers. Microservices modularity facilitates independent updates/deployments, and helps to avoid single point of failure, which can help prevent large-scale outages. We also decided to use Event Driven Architecture pattern which is a popular distributed asynchronous architecture pattern used to produce highly scalable applications. The event-driven architecture is made up of highly decoupled, single-purpose event processing components that asynchronously receive and process events.

      To build our #Backend capabilities we decided to use the following: 1. #Microservices - Java with Spring Boot , Node.js with ExpressJS and Python with Flask 2. #Eventsourcingframework - Amazon Kinesis , Amazon Kinesis Firehose , Amazon SNS , Amazon SQS, AWS Lambda 3. #Data - Amazon RDS , Amazon DynamoDB , Amazon S3 , MongoDB Atlas

      To build #Webapps we decided to use Angular 2 with RxJS

      #Devops - GitHub , Travis CI , Terraform , Docker , Serverless

      See more
      Salt logo

      Salt

      373
      350
      158
      Fast, scalable and flexible software for data center automation
      373
      350
      + 1
      158
      PROS OF SALT
      • 46
        Flexible
      • 29
        Easy
      • 26
        Remote execution
      • 23
        Enormously flexible
      • 12
        Great plugin API
      • 8
        Python
      • 5
        Extensible
      • 2
        Scalable
      • 2
        nginx
      • 1
        Best IaaC
      • 1
        Parallel Execution
      • 1
        Vagrant provisioner
      • 1
        Automatisation
      • 1
        HipChat
      CONS OF SALT
      • 1
        Bloated
      • 1
        Dangerous
      • 1
        No immutable infrastructure

      related Salt posts

      Shared insights
      on
      SaltSaltPuppet LabsPuppet LabsAnsibleAnsible
      at

      By 2014, the DevOps team at Lyft decided to port their infrastructure code from Puppet to Salt. At that point, the Puppet code based included around "10,000 lines of spaghetti-code,” which was unfamiliar and challenging to the relatively new members of the DevOps team.

      “The DevOps team felt that the Puppet infrastructure was too difficult to pick up quickly and would be impossible to introduce to [their] developers as the tool they’d use to manage their own services.”

      To determine a path forward, the team assessed both Ansible and Salt, exploring four key areas: simplicity/ease of use, maturity, performance, and community.

      They found that “Salt’s execution and state module support is more mature than Ansible’s, overall,” and that “Salt was faster than Ansible for state/playbook runs.” And while both have high levels of community support, Salt exceeded expectations in terms of friendless and responsiveness to opened issues.

      See more
      Neptune logo

      Neptune

      6
      17
      0
      The most lightweight experiment tracking tool for machine learning
      6
      17
      + 1
      0
      PROS OF NEPTUNE
        Be the first to leave a pro
        CONS OF NEPTUNE
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          related Neptune posts

          Neptune.io logo

          Neptune.io

          5
          10
          0
          Incident Response Automation for DevOps
          5
          10
          + 1
          0
          PROS OF NEPTUNE.IO
            Be the first to leave a pro
            CONS OF NEPTUNE.IO
              Be the first to leave a con

              related Neptune.io posts