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.
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What is Airflow and what are its top alternatives?

Apache Airflow is a popular open-source platform used for orchestrating complex computational workflows and data processing pipelines. It allows users to schedule and monitor workflows with ease, supports a wide range of integrations with popular technologies, and provides a rich library of operators for various tasks. However, Airflow can be complex to set up and maintain, especially for users new to the platform, and may require additional resources to manage effectively.

  1. Apache NiFi: Apache NiFi is an open-source data flow tool that provides a user-friendly interface for designing, scheduling, and monitoring data flows. Key features include data provenance, data security, and extensibility through custom processors. Pros: Easy to use interface, strong data provenance capabilities. Cons: Limited scalability compared to Airflow.
  2. Dagster: Dagster is a data orchestrator that focuses on building data applications. It provides a unified view of data pipelines, data quality checks, and operational metrics. Key features include a declarative pipeline definition language and a rich set of tools for monitoring and debugging pipelines. Pros: Strong focus on data quality, easy debugging capabilities. Cons: Limited support for non-data-centric workflows.
  3. Prefect: Prefect is a workflow orchestration tool that emphasizes simplicity and flexibility. It offers a Python-native API for defining and executing workflows, along with features like robust scheduling, parallel execution, and workflow versioning. Pros: Python-native API, easy to use, good support for parallel execution. Cons: Limited integrations compared to Airflow.
  4. Luigi: Luigi is a Python-based workflow scheduler that was developed by Spotify. It simplifies the process of defining complex pipelines through a Python class hierarchy and supports task dependency management. Key features include visualizations of workflow dependencies and extensibility through custom tasks. Pros: Simple to define workflows, good task dependency management. Cons: Lack of built-in support for monitoring and scaling.
  5. Kubeflow Pipelines: Kubeflow Pipelines is an open-source framework for building and deploying machine learning workflows on Kubernetes. It provides a visual interface for composing ML pipelines, along with features like versioning, artifact tracking, and model serving. Pros: Native Kubernetes support, seamless integration with ML tools. Cons: Limited support for non-ML workflows.
  6. Pinball: Pinball is a flexible workflow manager that allows users to define, schedule, and run tasks in a distributed environment. It provides a simple API for creating workflows, supports multiple scheduling strategies, and offers tools for monitoring and debugging tasks. Pros: Flexible task scheduling, easy to use API. Cons: Limited scalability for large-scale workflows compared to Airflow.
  7. TaskFlow: TaskFlow is a Python library for defining and executing task flows in a distributed system. It offers features like task dependencies, fault tolerance, and flow control mechanisms. Pros: Lightweight library, good fault tolerance capabilities. Cons: Limited support for complex workflow orchestration compared to Airflow.
  8. Cadence: Cadence is a scalable distributed workflow orchestrator that was open-sourced by Uber. It provides features like long-running workflows, state management, and multi-language support. Key features include retry policies, domain-specific workflows, and distributed data processing. Pros: Scalable, highly fault-tolerant, multi-language support. Cons: Complex setup and configuration compared to Airflow.
  9. Argo Workflows: Argo Workflows is an open-source workflow orchestration tool that runs on Kubernetes. It supports defining complex workflows as Docker containers or Kubernetes deployments and offers features like DAG visualization, workflow templates, and step retry policies. Pros: Native Kubernetes support, easy integration with containerized workflows. Cons: Limited support for non-containerized workflows.
  10. Rundeck: Rundeck is an open-source automation tool that provides workflow orchestration, job scheduling, and execution capabilities. It allows users to define reusable workflows through a web-based interface, supports various job types and options for scheduling, and offers features like access control and audit trails. Pros: User-friendly interface, good job scheduling capabilities. Cons: Limited support for complex data processing workflows compared to Airflow.

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

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ETL and data flow management library
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PROS OF LUIGI
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    Hadoop Support
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    Python
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    Open soure
CONS OF LUIGI
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    related Luigi posts

    Apache NiFi logo

    Apache NiFi

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

    related Apache NiFi posts

    John Calandra
    Data Manager at The Garrett Group · | 8 upvotes · 360.8K views

    There is a question coming... I am using Oracle VirtualBox to spawn 3 Ubuntu Linux virtual machines (VM). VM1 is being used as a data lake - just a place to store flat files. VM2 hosts Apache NiFi. VM3 hosts PostgreSQL. I have built a NiFi pipeline that reads flat files on VM1 and then pipes the data over to and inserts it into the Postgresql database. I left this setup alone for a while, and then something hiccupped on VM3, and I had to rebuild it. Now I cannot make a remote connection to Postgresql on VM3. I was using pgAdmin3 on VM3, but it kept throwing errors - I found out it went end-of-life in 2018 and uninstalled it. pgAdmin4 is out, but for some reason, I cannot get the APT utility to find/install it. I am trying to figure out the pgAdmin4 install problem and looking for a good alternative for pgAdmin4 that I can use to diagnose the remote database connection problem. Does anyone have any suggestions? Thanks in advance.

    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?

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    Jenkins logo

    Jenkins

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    An extendable open source continuous integration server
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    PROS OF JENKINS
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      Hosted internally
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      Free open source
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      Great to build, deploy or launch anything async
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      Tons of integrations
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      Rich set of plugins with good documentation
    • 111
      Has support for build pipelines
    • 68
      Easy setup
    • 66
      It is open-source
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      Workflow plugin
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      Configuration as code
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      Very powerful tool
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      Many Plugins
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      Continuous Integration
    • 10
      Great flexibility
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      Git and Maven integration is better
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      100% free and open source
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      Slack Integration (plugin)
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      Github integration
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      Self-hosted GitLab Integration (plugin)
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      Easy customisation
    • 5
      Pipeline API
    • 5
      Docker support
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      Fast builds
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      Hosted Externally
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      Excellent docker integration
    • 4
      Platform idnependency
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      AWS Integration
    • 3
      JOBDSL
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      It's Everywhere
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      Customizable
    • 3
      Can be run as a Docker container
    • 3
      It`w worked
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      Loose Coupling
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      NodeJS Support
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      Build PR Branch Only
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      Easily extendable with seamless integration
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      PHP Support
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      Ruby/Rails Support
    • 2
      Universal controller
    CONS OF JENKINS
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      Workarounds needed for basic requirements
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      Groovy with cumbersome syntax
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      Plugins compatibility issues
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      Lack of support
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      Limited abilities with declarative pipelines
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      No YAML syntax
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      Too tied to plugins versions

    related Jenkins posts

    Tymoteusz Paul
    Devops guy at X20X Development LTD · | 23 upvotes · 8.9M views

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

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

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

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

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

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

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

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    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

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    AWS Step Functions logo

    AWS Step Functions

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    Build Distributed Applications Using Visual Workflows
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    PROS OF AWS STEP FUNCTIONS
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      Integration with other services
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      Easily Accessible via AWS Console
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      Complex workflows
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      Pricing
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      Scalability
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      Workflow Processing
    • 3
      High Availability
    CONS OF AWS STEP FUNCTIONS
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      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?

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      Matheus Moreira
      Backend Engineer at IntuitiveCare · | 5 upvotes · 240.5K views
      Shared insights
      on
      AWS Step FunctionsAWS Step FunctionsAirflowAirflow

      We have some lambdas we need to orchestrate to get our workflow going. In the past, we already attempted to use Airflow as the orchestrator, but the need to coordinate the tasks in a database generates an overhead that we cannot afford. For our use case, there are hundreds of inputs per minute and we need to scale to support all the inputs and have an efficient way to analyze them later. The ideal product would be AWS Step Functions since it can manage our load demand graciously, but it is too expensive and we cannot afford that. So, I would like to get alternatives for an orchestrator that does not need a complex backend, can manage hundreds of inputs per minute, and is not too expensive.

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      Pachyderm logo

      Pachyderm

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      MapReduce without Hadoop. Analyze massive datasets with Docker.
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      PROS OF PACHYDERM
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        Containers
      • 1
        Versioning
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        Can run on GCP or AWS
      CONS OF PACHYDERM
      • 1
        Recently acquired by HPE, uncertain future.

      related Pachyderm posts

      Kubeflow logo

      Kubeflow

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      Machine Learning Toolkit for Kubernetes
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      PROS OF KUBEFLOW
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        System designer
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        Google backed
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        Customisation
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        Kfp dsl
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        Azure
      CONS OF KUBEFLOW
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        related Kubeflow posts

        Biswajit Pathak
        Project Manager at Sony · | 6 upvotes · 847.2K views

        Can you please advise which one to choose FastText Or Gensim, in terms of:

        1. Operability with ML Ops tools such as MLflow, Kubeflow, etc.
        2. Performance
        3. Customization of Intermediate steps
        4. FastText and Gensim both have the same underlying libraries
        5. Use cases each one tries to solve
        6. Unsupervised Vs Supervised dimensions
        7. Ease of Use.

        Please mention any other points that I may have missed here.

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        Shared insights
        on
        KubeflowKubeflowKubernetesKubernetesMLflowMLflow

        We are trying to standardise DevOps across both ML (model selection and deployment) and regular software. Want to minimise the number of tools we have to learn. Also want a scalable solution which is easy enough to start small - eg. on a powerful laptop and eventually be deployed at scale. MLflow vs Kubernetes (Kubeflow)?

        See more
        Argo logo

        Argo

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        Container-native workflows for Kubernetes
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        PROS OF ARGO
        • 3
          Open Source
        • 2
          Autosinchronize the changes to deploy
        • 1
          Online service, no need to install anything
        CONS OF ARGO
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          Kafka logo

          Kafka

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          Distributed, fault tolerant, high throughput pub-sub messaging system
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          PROS OF KAFKA
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            High-throughput
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            Distributed
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            Scalable
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            High-Performance
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            Durable
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            Publish-Subscribe
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            Simple-to-use
          • 18
            Open source
          • 12
            Written in Scala and java. Runs on JVM
          • 9
            Message broker + Streaming system
          • 4
            KSQL
          • 4
            Avro schema integration
          • 4
            Robust
          • 3
            Suport Multiple clients
          • 2
            Extremely good parallelism constructs
          • 2
            Partioned, replayable log
          • 1
            Simple publisher / multi-subscriber model
          • 1
            Fun
          • 1
            Flexible
          CONS OF KAFKA
          • 32
            Non-Java clients are second-class citizens
          • 29
            Needs Zookeeper
          • 9
            Operational difficulties
          • 5
            Terrible Packaging

          related Kafka posts

          Nick Rockwell
          SVP, Engineering at Fastly · | 46 upvotes · 3.6M views

          When I joined NYT there was already broad dissatisfaction with the LAMP (Linux Apache HTTP Server MySQL PHP) Stack and the front end framework, in particular. So, I wasn't passing judgment on it. I mean, LAMP's fine, you can do good work in LAMP. It's a little dated at this point, but it's not ... I didn't want to rip it out for its own sake, but everyone else was like, "We don't like this, it's really inflexible." And I remember from being outside the company when that was called MIT FIVE when it had launched. And been observing it from the outside, and I was like, you guys took so long to do that and you did it so carefully, and yet you're not happy with your decisions. Why is that? That was more the impetus. If we're going to do this again, how are we going to do it in a way that we're gonna get a better result?

          So we're moving quickly away from LAMP, I would say. So, right now, the new front end is React based and using Apollo. And we've been in a long, protracted, gradual rollout of the core experiences.

          React is now talking to GraphQL as a primary API. There's a Node.js back end, to the front end, which is mainly for server-side rendering, as well.

          Behind there, the main repository for the GraphQL server is a big table repository, that we call Bodega because it's a convenience store. And that reads off of a Kafka pipeline.

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          Ashish Singh
          Tech Lead, Big Data Platform at Pinterest · | 38 upvotes · 3M views

          To provide employees with the critical need of interactive querying, we’ve worked with Presto, an open-source distributed SQL query engine, over the years. Operating Presto at Pinterest’s scale has involved resolving quite a few challenges like, supporting deeply nested and huge thrift schemas, slow/ bad worker detection and remediation, auto-scaling cluster, graceful cluster shutdown and impersonation support for ldap authenticator.

          Our infrastructure is built on top of Amazon EC2 and we leverage Amazon S3 for storing our data. This separates compute and storage layers, and allows multiple compute clusters to share the S3 data.

          We have hundreds of petabytes of data and tens of thousands of Apache Hive tables. Our Presto clusters are comprised of a fleet of 450 r4.8xl EC2 instances. Presto clusters together have over 100 TBs of memory and 14K vcpu cores. Within Pinterest, we have close to more than 1,000 monthly active users (out of total 1,600+ Pinterest employees) using Presto, who run about 400K queries on these clusters per month.

          Each query submitted to Presto cluster is logged to a Kafka topic via Singer. Singer is a logging agent built at Pinterest and we talked about it in a previous post. Each query is logged when it is submitted and when it finishes. When a Presto cluster crashes, we will have query submitted events without corresponding query finished events. These events enable us to capture the effect of cluster crashes over time.

          Each Presto cluster at Pinterest has workers on a mix of dedicated AWS EC2 instances and Kubernetes pods. Kubernetes platform provides us with the capability to add and remove workers from a Presto cluster very quickly. The best-case latency on bringing up a new worker on Kubernetes is less than a minute. However, when the Kubernetes cluster itself is out of resources and needs to scale up, it can take up to ten minutes. Some other advantages of deploying on Kubernetes platform is that our Presto deployment becomes agnostic of cloud vendor, instance types, OS, etc.

          #BigData #AWS #DataScience #DataEngineering

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