Scheduling tools for ETL and ML flow

This page summarizes the projects mentioned and recommended in the original post on /r/dataengineering

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  • dagster

    An orchestration platform for the development, production, and observation of data assets.

  • I would give dagster a look. It has a built-in native scheduler and is cross-platform. It is general purpose, so your team can grow with it and tackle broader set of use cases if needed. If you struggle to get started after reading their docs/tutorials, you can take a look at my personal repo. Ive gotten a few feedback that my example has been very useful in getting started. I know they revamped their docs recently, but havent looked at their tutorial again or looked to see if they provided an intermediate level full example yet, so I need to get back in there to see.

  • dagster-sklearn

    dagster scikit-learn pipeline example.

  • I would give dagster a look. It has a built-in native scheduler and is cross-platform. It is general purpose, so your team can grow with it and tackle broader set of use cases if needed. If you struggle to get started after reading their docs/tutorials, you can take a look at my personal repo. Ive gotten a few feedback that my example has been very useful in getting started. I know they revamped their docs recently, but havent looked at their tutorial again or looked to see if they provided an intermediate level full example yet, so I need to get back in there to see.

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    Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

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  • ploomber

    The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️

  • If you want the longer version. Take a look at the survey I made on workflow management tools. Also check out Ploomber, the project I'm working on, which is tailored for DS/ML workflows.

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

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