Alternatives to Airflow logo

Alternatives to Airflow

Luigi, Apache NiFi, Jenkins, AWS Step Functions, and Pachyderm are the most popular alternatives and competitors to Airflow.
1.1K
1.8K
+ 1
107

What is Airflow and what are its top alternatives?

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.
Airflow is a tool in the Workflow Manager category of a tech stack.
Airflow is an open source tool with 21.7K GitHub stars and 8.6K GitHub forks. Here’s a link to Airflow's open source repository on GitHub

Top Alternatives to Airflow

  • Luigi

    Luigi

    It is a Python module that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization etc. It also comes with Hadoop support built in. ...

  • Apache NiFi

    Apache NiFi

    An easy to use, powerful, and reliable system to process and distribute data. It supports powerful and scalable directed graphs of data routing, transformation, and system mediation logic. ...

  • 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. ...

  • AWS Step Functions

    AWS Step Functions

    AWS Step Functions makes it easy to coordinate the components of distributed applications and microservices using visual workflows. Building applications from individual components that each perform a discrete function lets you scale and change applications quickly. ...

  • Pachyderm

    Pachyderm

    Pachyderm is an open source MapReduce engine that uses Docker containers for distributed computations. ...

  • Kubeflow

    Kubeflow

    The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable by providing a straightforward way for spinning up best of breed OSS solutions. ...

  • Argo

    Argo

    Argo is an open source container-native workflow engine for getting work done on Kubernetes. Argo is implemented as a Kubernetes CRD (Custom Resource Definition). ...

  • Kafka

    Kafka

    Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design. ...

Airflow alternatives & related posts

Luigi logo

Luigi

52
122
7
ETL and data flow management library
52
122
+ 1
7
PROS OF LUIGI
  • 4
    Hadoop Support
  • 2
    Python
  • 1
    Open soure
CONS OF LUIGI
    Be the first to leave a con

    related Luigi posts

    Apache NiFi logo

    Apache NiFi

    234
    479
    55
    A reliable system to process and distribute data
    234
    479
    + 1
    55
    PROS OF APACHE NIFI
    • 14
      Visual Data Flows using Directed Acyclic Graphs (DAGs)
    • 7
      Free (Open Source)
    • 5
      Simple-to-use
    • 4
      Reactive with back-pressure
    • 4
      Scalable horizontally as well as vertically
    • 3
      Bi-directional channels
    • 3
      Fast prototyping
    • 2
      Data provenance
    • 2
      Built-in graphical user interface
    • 2
      End-to-end security between all nodes
    • 2
      Can handle messages up to gigabytes in size
    • 1
      Hbase support
    • 1
      Kudu support
    • 1
      Hive support
    • 1
      Slack integration
    • 1
      Support for custom Processor in Java
    • 1
      Lot of articles
    • 1
      Lots of documentation
    CONS OF APACHE NIFI
    • 2
      Memory-intensive
    • 1
      HA support is not full fledge

    related Apache NiFi posts

    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

    39.2K
    31.9K
    2.2K
    An extendable open source continuous integration server
    39.2K
    31.9K
    + 1
    2.2K
    PROS OF JENKINS
    • 521
      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
    • 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
      Great flexibility
    • 8
      Git and Maven integration is better
    • 7
      Continuous Integration
    • 6
      Github integration
    • 6
      Slack Integration (plugin)
    • 5
      100% free and open source
    • 5
      Self-hosted GitLab Integration (plugin)
    • 5
      Easy customisation
    • 4
      Docker support
    • 3
      Pipeline API
    • 3
      Excellent docker integration
    • 3
      Platform idnependency
    • 3
      Fast builds
    • 2
      Hosted Externally
    • 2
      It`w worked
    • 2
      Can be run as a Docker container
    • 2
      Customizable
    • 2
      AWS Integration
    • 2
      It's Everywhere
    • 2
      JOBDSL
    • 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
      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 · 4.3M views

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

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

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

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

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

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

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

    See more
    AWS Step Functions logo

    AWS Step Functions

    158
    279
    22
    Build Distributed Applications Using Visual Workflows
    158
    279
    + 1
    22
    PROS OF AWS STEP FUNCTIONS
    • 5
      Integration with other services
    • 4
      Pricing
    • 4
      Easily Accessible via AWS Console
    • 3
      Complex workflows
    • 2
      Scalability
    • 2
      High Availability
    • 2
      Workflow Processing
    CONS OF AWS STEP FUNCTIONS
      Be the first to leave a con

      related AWS Step Functions posts

      Shared insights
      on
      AWS Step Functions
      Airflow

      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
      Pachyderm logo

      Pachyderm

      16
      54
      5
      MapReduce without Hadoop. Analyze massive datasets with Docker.
      16
      54
      + 1
      5
      PROS OF PACHYDERM
      • 3
        Containers
      • 1
        Versioning
      • 1
        Can run on GCP or AWS
      CONS OF PACHYDERM
        Be the first to leave a con

        related Pachyderm posts

        Kubeflow logo

        Kubeflow

        130
        433
        13
        Machine Learning Toolkit for Kubernetes
        130
        433
        + 1
        13
        PROS OF KUBEFLOW
        • 5
          System designer
        • 3
          Customisation
        • 3
          Kfp dsl
        • 2
          Google backed
        CONS OF KUBEFLOW
          Be the first to leave a con

          related Kubeflow posts

          Amazon SageMaker constricts the use of their own mxnet package and does not offer a strong Kubernetes backbone. At the same time, Kubeflow is still quite buggy and cumbersome to use. Which tool is a better pick for MLOps pipelines (both from the perspective of scalability and depth)?

          See more
          Argo logo

          Argo

          184
          195
          0
          Container-native workflows for Kubernetes
          184
          195
          + 1
          0
          PROS OF ARGO
            Be the first to leave a pro
            CONS OF ARGO
              Be the first to leave a con

              related Argo posts

              Kafka logo

              Kafka

              13.9K
              13K
              557
              Distributed, fault tolerant, high throughput pub-sub messaging system
              13.9K
              13K
              + 1
              557
              PROS OF KAFKA
              • 119
                High-throughput
              • 113
                Distributed
              • 85
                Scalable
              • 78
                High-Performance
              • 64
                Durable
              • 35
                Publish-Subscribe
              • 17
                Simple-to-use
              • 14
                Open source
              • 10
                Written in Scala and java. Runs on JVM
              • 6
                Message broker + Streaming system
              • 4
                Avro schema integration
              • 2
                Suport Multiple clients
              • 2
                Robust
              • 2
                KSQL
              • 2
                Partioned, replayable log
              • 1
                Fun
              • 1
                Extremely good parallelism constructs
              • 1
                Simple publisher / multi-subscriber model
              • 1
                Flexible
              CONS OF KAFKA
              • 27
                Non-Java clients are second-class citizens
              • 26
                Needs Zookeeper
              • 7
                Operational difficulties
              • 2
                Terrible Packaging

              related Kafka posts

              Eric Colson
              Chief Algorithms Officer at Stitch Fix · | 21 upvotes · 1.8M views

              The algorithms and data infrastructure at Stitch Fix is housed in #AWS. Data acquisition is split between events flowing through Kafka, and periodic snapshots of PostgreSQL DBs. We store data in an Amazon S3 based data warehouse. Apache Spark on Yarn is our tool of choice for data movement and #ETL. Because our storage layer (s3) is decoupled from our processing layer, we are able to scale our compute environment very elastically. We have several semi-permanent, autoscaling Yarn clusters running to serve our data processing needs. While the bulk of our compute infrastructure is dedicated to algorithmic processing, we also implemented Presto for adhoc queries and dashboards.

              Beyond data movement and ETL, most #ML centric jobs (e.g. model training and execution) run in a similarly elastic environment as containers running Python and R code on Amazon EC2 Container Service clusters. The execution of batch jobs on top of ECS is managed by Flotilla, a service we built in house and open sourced (see https://github.com/stitchfix/flotilla-os).

              At Stitch Fix, algorithmic integrations are pervasive across the business. We have dozens of data products actively integrated systems. That requires serving layer that is robust, agile, flexible, and allows for self-service. Models produced on Flotilla are packaged for deployment in production using Khan, another framework we've developed internally. Khan provides our data scientists the ability to quickly productionize those models they've developed with open source frameworks in Python 3 (e.g. PyTorch, sklearn), by automatically packaging them as Docker containers and deploying to Amazon ECS. This provides our data scientist a one-click method of getting from their algorithms to production. We then integrate those deployments into a service mesh, which allows us to A/B test various implementations in our product.

              For more info:

              #DataScience #DataStack #Data

              See more
              John Kodumal

              As we've evolved or added additional infrastructure to our stack, we've biased towards managed services. Most new backing stores are Amazon RDS instances now. We do use self-managed PostgreSQL with TimescaleDB for time-series data—this is made HA with the use of Patroni and Consul.

              We also use managed Amazon ElastiCache instances instead of spinning up Amazon EC2 instances to run Redis workloads, as well as shifting to Amazon Kinesis instead of Kafka.

              See more