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Posted to dev@beam.apache.org by Mohamed Awnallah <mo...@gmail.com> on 2025/09/12 13:16:27 UTC

[Proposal][GSoC 2025] Tecton Feature Store Sink I/O Connector for Beam

Hello Beam Dev Community,

I'm happy to share the design document for the Milvus Vector Sink I/O
Connector for Apache Beam, developed as part of my GSoC 2025 project.

This sink I/O connector enables both streaming and batch pipelines to
persist features into Tecton's online and offline feature stores. It
integrates Tecton's capabilities into Beam workflows, allowing pipelines to
store newly computed or updated feature data. It is essential for enabling
real-time feature serving, continuous feature engineering, and managing the
full lifecycle of feature data in modern machine learning systems.

Here is the link to the design document:
https://docs.google.com/document/d/16MvDv1EQuNosucsLA2KA4PXYxN8GunAHhTOp5HMIcks/edit?usp=sharing

This implementation is part of the GSoC 2025 ML Integration project being
tracked here:
https://github.com/apache/beam/issues/35046

I welcome any feedback, suggestions, or questions about the design approach.

Thank you,
Mohamed

Re: [Proposal][GSoC 2025] Tecton Feature Store Sink I/O Connector for Beam

Posted by Mohamed Awnallah <mo...@gmail.com>.

Sorry for mistake by mentioning Milvus. It was meant for Tecton Sink I/O

On Friday, September 12, 2025, Mohamed Awnallah <mo...@gmail.com>
wrote:

> Hello Beam Dev Community,
>
> I'm happy to share the design document for the Milvus Vector Sink I/O
> Connector for Apache Beam, developed as part of my GSoC 2025 project.
>
> This sink I/O connector enables both streaming and batch pipelines to
> persist features into Tecton's online and offline feature stores. It
> integrates Tecton's capabilities into Beam workflows, allowing pipelines to
> store newly computed or updated feature data. It is essential for enabling
> real-time feature serving, continuous feature engineering, and managing the
> full lifecycle of feature data in modern machine learning systems.
>
> Here is the link to the design document:
> https://docs.google.com/document/d/16MvDv1EQuNosucsLA2KA4PXYxN8Gu
> nAHhTOp5HMIcks/edit?usp=sharing
>
> This implementation is part of the GSoC 2025 ML Integration project being
> tracked here:
> https://github.com/apache/beam/issues/35046
>
> I welcome any feedback, suggestions, or questions about the design
> approach.
>
> Thank you,
> Mohamed
>
>