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Airflow vs Kubeflow: What are the differences?


Airflow and Kubeflow are both popular tools used in data engineering and data science workflows. While they both have the goal of managing and orchestrating complex workflows, there are several key differences between the two that set them apart and make them suitable for different use cases.

  1. Architecture: Airflow is a task scheduler and workflow management platform that uses Directed Acyclic Graphs (DAGs) to define and execute tasks. It runs on a centralized server and relies on a scheduler to trigger task executions. On the other hand, Kubeflow is an open-source machine learning toolkit that runs natively on Kubernetes. It leverages the container orchestration capabilities of Kubernetes to distribute and scale workloads.

  2. Decentralized Execution: In Airflow, tasks are executed by workers running on separate machines or nodes. The tasks are scheduled and coordinated by the central Airflow server. Kubeflow, however, enables decentralized execution by running tasks within containers on a Kubernetes cluster. This allows for efficient resource allocation, scaling, and fault tolerance.

  3. Focus: Airflow primarily focuses on workflow management and scheduling, allowing users to define and orchestrate tasks. It provides a rich set of operators and connectors for integration with various systems and services. Kubeflow, on the other hand, is specifically designed for the deployment and management of machine learning workflows. It provides tools and components tailored to the machine learning lifecycle, such as data preprocessing, model training, and serving.

  4. Integration with Kubernetes: While Airflow can run on Kubernetes to achieve containerization and scalability, it is not tightly integrated with Kubernetes as a native solution. In contrast, Kubeflow is built on top of Kubernetes and leverages its features for container orchestration, automatic scaling, and workload management. Kubeflow also provides additional components, such as Kubeflow Pipelines, for building and deploying machine learning workflows.

  5. Community and Ecosystem: Airflow has a mature and active community with a wide range of contributed operators, connections, and plugins. It has been extensively adopted and used by many organizations. Kubeflow, being a more specialized tool, has a growing community focused on machine learning workflows. It offers integration with popular machine learning frameworks and libraries and benefits from the broader Kubernetes ecosystem.

  6. Use Cases: Airflow is suitable for a variety of use cases beyond machine learning, such as data pipelines, ETL (Extract, Transform, Load) processes, and workflow automation. It provides flexibility and extensibility for diverse data engineering and data science workflows. Kubeflow, on the other hand, shines in the machine learning domain, providing features specifically tailored for building, training, and serving machine learning models at scale.

In Summary, Airflow and Kubeflow differ in their architecture, execution model, focus, integration with Kubernetes, community, and use cases. While Airflow is a general-purpose workflow management platform, Kubeflow is a specialized toolkit for machine learning workflows on Kubernetes.

Advice on Airflow and Kubeflow
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Apache SparkApache Spark

I am so confused. I need a tool that will allow me to go to about 10 different URLs to get a list of objects. Those object lists will be hundreds or thousands in length. I then need to get detailed data lists about each object. Those detailed data lists can have hundreds of elements that could be map/reduced somehow. My batch process dies sometimes halfway through which means hours of processing gone, i.e. time wasted. I need something like a directed graph that will keep results of successful data collection and allow me either pragmatically or manually to retry the failed ones some way (0 - forever) times. I want it to then process all the ones that have succeeded or been effectively ignored and load the data store with the aggregation of some couple thousand data-points. I know hitting this many endpoints is not a good practice but I can't put collectors on all the endpoints or anything like that. It is pretty much the only way to get the data.

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Gilroy Gordon
Solution Architect at IGonics Limited · | 2 upvotes · 268.1K views

For a non-streaming approach:

You could consider using more checkpoints throughout your spark jobs. Furthermore, you could consider separating your workload into multiple jobs with an intermittent data store (suggesting cassandra or you may choose based on your choice and availability) to store results , perform aggregations and store results of those.

Spark Job 1 - Fetch Data From 10 URLs and store data and metadata in a data store (cassandra) Spark Job 2..n - Check data store for unprocessed items and continue the aggregation

Alternatively for a streaming approach: Treating your data as stream might be useful also. Spark Streaming allows you to utilize a checkpoint interval -

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Pros of Airflow
Pros of Kubeflow
  • 51
  • 14
    Task Dependency Management
  • 12
    Beautiful UI
  • 12
    Cluster of workers
  • 10
  • 6
    Open source
  • 5
    Complex workflows
  • 5
  • 3
    Good api
  • 3
    Apache project
  • 3
    Custom operators
  • 2
  • 9
    System designer
  • 3
    Google backed
  • 3
  • 3
    Kfp dsl
  • 0

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Cons of Airflow
Cons of Kubeflow
  • 2
    Observability is not great when the DAGs exceed 250
  • 2
    Running it on kubernetes cluster relatively complex
  • 2
    Open source - provides minimum or no support
  • 1
    Logical separation of DAGs is not straight forward
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    What is 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.

    What is 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.

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