Alternatives to StackStorm logo

Alternatives to StackStorm

Ansible, Rundeck, Airflow, Jenkins, and Terraform are the most popular alternatives and competitors to StackStorm.
68
144
+ 1
28

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 4.6K GitHub stars and 623 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

14K
11.2K
1.3K
Radically simple configuration-management, application deployment, task-execution, and multi-node orchestration engine
14K
11.2K
+ 1
1.3K
PROS OF ANSIBLE
  • 276
    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
  • 2
    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
  • 3
    Backward compatibility
  • 2
    Doesn't Run on Windows
  • 2
    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.

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

163
247
7
A platform for Self-Service Operations
163
247
+ 1
7
PROS OF RUNDECK
  • 3
    Easy to understand
  • 3
    Role based access control
  • 1
    Doesn't need containers
CONS OF RUNDECK
    Be the first to leave a con

    related Rundeck posts

    Airflow logo

    Airflow

    1.2K
    2K
    113
    A platform to programmaticaly author, schedule and monitor data pipelines, by Airbnb
    1.2K
    2K
    + 1
    113
    PROS OF AIRFLOW
    • 44
      Features
    • 13
      Task Dependency Management
    • 12
      Beautiful UI
    • 11
      Cluster of workers
    • 10
      Extensibility
    • 5
      Open source
    • 4
      Python
    • 4
      Complex workflows
    • 3
      K
    • 2
      Dashboard
    • 2
      Good api
    • 2
      Custom operators
    • 1
      Apache project
    CONS OF AIRFLOW
    • 1
      Open source - provides minimum or no support
    • 1
      Logical separation of DAGs is not straight forward
    • 1
      Running it on kubernetes cluster relatively complex
    • 1
      Observability is not great when the DAGs exceed 250

    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
    Shared insights
    on
    AWS Step FunctionsAWS Step FunctionsAirflowAirflow

    I am working on a project that grabs a set of input data from AWS S3, pre-processes and divvies it up, spins up 10K batch containers to process the divvied data in parallel on AWS Batch, post-aggregates the data, and pushes it to S3.

    I already have software patterns from other projects for Airflow + Batch but have not dealt with the scaling factors of 10k parallel tasks. Airflow is nice since I can look at which tasks failed and retry a task after debugging. But dealing with that many tasks on one Airflow EC2 instance seems like a barrier. Another option would be to have one task that kicks off the 10k containers and monitors it from there.

    I have no experience with AWS Step Functions but have heard it's AWS's Airflow. There looks to be plenty of patterns online for Step Functions + Batch. Do Step Functions seem like a good path to check out for my use case? Do you get the same insights on failing jobs / ability to retry tasks as you do with Airflow?

    See more
    Jenkins logo

    Jenkins

    43.1K
    35.7K
    2.2K
    An extendable open source continuous integration server
    43.1K
    35.7K
    + 1
    2.2K
    PROS OF JENKINS
    • 522
      Hosted internally
    • 465
      Free open source
    • 315
      Great to build, deploy or launch anything async
    • 243
      Tons of integrations
    • 210
      Rich set of plugins with good documentation
    • 109
      Has support for build pipelines
    • 72
      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
      Continuous Integration
    • 8
      Great flexibility
    • 8
      Git and Maven integration is better
    • 6
      Github integration
    • 6
      100% free and open source
    • 6
      Slack Integration (plugin)
    • 5
      Easy customisation
    • 5
      Self-hosted GitLab Integration (plugin)
    • 4
      Docker support
    • 3
      Excellent docker integration
    • 3
      Platform idnependency
    • 3
      Fast builds
    • 3
      Pipeline API
    • 2
      Customizable
    • 2
      Can be run as a Docker container
    • 2
      It`w worked
    • 2
      JOBDSL
    • 2
      Hosted Externally
    • 2
      It's Everywhere
    • 2
      AWS Integration
    • 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
    • 8
      Groovy with cumbersome syntax
    • 6
      Limited abilities with declarative pipelines
    • 6
      Plugins compatibility issues
    • 5
      Lack of support
    • 4
      No YAML syntax
    • 2
      Too tied to plugins versions

    related Jenkins 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.

    See more
    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
    Terraform logo

    Terraform

    11.2K
    8.3K
    319
    Describe your complete infrastructure as code and build resources across providers
    11.2K
    8.3K
    + 1
    319
    PROS OF TERRAFORM
    • 109
      Infrastructure as code
    • 72
      Declarative syntax
    • 44
      Planning
    • 27
      Simple
    • 24
      Parallelism
    • 7
      Cloud agnostic
    • 6
      Well-documented
    • 6
      It's like coding your infrastructure in simple English
    • 4
      Automates infrastructure deployments
    • 4
      Immutable infrastructure
    • 4
      Platform agnostic
    • 3
      Extendable
    • 3
      Automation
    • 3
      Portability
    • 2
      Lightweight
    • 2
      Scales to hundreds of hosts
    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
    Emanuel Evans
    Senior Architect at Rainforest QA · | 16 upvotes · 697.2K views

    We recently moved our main applications from Heroku to Kubernetes . The 3 main driving factors behind the switch were scalability (database size limits), security (the inability to set up PostgreSQL instances in private networks), and costs (GCP is cheaper for raw computing resources).

    We prefer using managed services, so we are using Google Kubernetes Engine with Google Cloud SQL for PostgreSQL for our PostgreSQL databases and Google Cloud Memorystore for Redis . For our CI/CD pipeline, we are using CircleCI and Google Cloud Build to deploy applications managed with Helm . The new infrastructure is managed with Terraform .

    Read the blog post to go more in depth.

    See more
    Salt logo

    Salt

    386
    378
    164
    Fast, scalable and flexible software for data center automation
    386
    378
    + 1
    164
    PROS OF SALT
    • 47
      Flexible
    • 30
      Easy
    • 27
      Remote execution
    • 24
      Enormously flexible
    • 12
      Great plugin API
    • 9
      Python
    • 5
      Extensible
    • 3
      Scalable
    • 2
      nginx
    • 1
      Vagrant provisioner
    • 1
      HipChat
    • 1
      Best IaaC
    • 1
      Automatisation
    • 1
      Parallel Execution
    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

    9
    25
    2
    The most lightweight experiment tracking tool for machine learning
    9
    25
    + 1
    2
    PROS OF NEPTUNE
    • 1
      Aws managed services
    • 1
      Supports both gremlin and openCypher query languages
    CONS OF NEPTUNE
    • 1
      Doesn't have much support for openCypher clients
    • 1
      Doesn't have proper clients for different lanuages
    • 1
      Doesn't have much community support

    related Neptune posts

    Neptune.io logo

    Neptune.io

    5
    9
    0
    Incident Response Automation for DevOps
    5
    9
    + 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