What’s your process for deploying a data pipeline from a notebook, running it, and managing it in production?

• datahub

  34 9,197 9.9 Java

  The Metadata Platform for your Data Stack

  

Something like this? https://datahubproject.io/

• Mage

  77 7,001 9.9 Python

  🧙 The modern replacement for Airflow. Mage is an open-source data pipeline tool for transforming and integrating data. https://github.com/mage-ai/mage-ai

  

Reverse ETL: new hot one https://github.com/mage-ai/mage-ai

• WorkOS

  workos.com sponsored

  The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.

  

• featureform

  28 1,674 9.7 Jupyter Notebook

  The Virtual Feature Store. Turn your existing data infrastructure into a feature store.

  

Feature store: new hot one: https://www.featureform.com/

• neon

  122 12,186 9.9 Rust

  Neon: Serverless Postgres. We separated storage and compute to offer autoscaling, branching, and bottomless storage.

  

App DBs: new hot one on the scene https://neon.tech/

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