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Airflow

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Kubeflow

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Airflow vs Kubeflow: What are the differences?

Airflow: A platform to programmaticaly author, schedule and monitor data pipelines, by Airbnb. 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; Kubeflow: Machine Learning Toolkit for Kubernetes. 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.

Airflow and Kubeflow are primarily classified as "Workflow Manager" and "Machine Learning" tools respectively.

Airflow and Kubeflow are both open source tools. It seems that Airflow with 13.3K GitHub stars and 4.91K forks on GitHub has more adoption than Kubeflow with 7.23K GitHub stars and 1.08K GitHub forks.

Airbnb, Slack, and 9GAG are some of the popular companies that use Airflow, whereas Kubeflow is used by Eliiza, Hepsiburada, and Big Insight. Airflow has a broader approval, being mentioned in 98 company stacks & 162 developers stacks; compared to Kubeflow, which is listed in 3 company stacks and 8 developer stacks.

Advice on Airflow and Kubeflow
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Apache Spark
Luigi
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Airflow

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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Replies (1)
Gilroy Gordon
Solution Architect at IGonics Limited · | 2 upvotes · 122.8K views
Recommends
Cassandra

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 - https://spark.apache.org/docs/latest/streaming-programming-guide.html#checkpointing

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Pros of Airflow
Pros of Kubeflow
  • 43
    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
  • 8
    System designer
  • 3
    Customisation
  • 3
    Kfp dsl
  • 2
    Google backed

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Cons of Airflow
Cons of Kubeflow
  • 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
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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.

    Need advice about which tool to choose?Ask the StackShare community!

    What companies use Airflow?
    What companies use Kubeflow?
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    What tools integrate with Airflow?
    What tools integrate with Kubeflow?

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    What are some alternatives to Airflow and Kubeflow?
    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
    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
    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 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 is an open source MapReduce engine that uses Docker containers for distributed computations.
    See all alternatives