Hi Beam community, With my GSoC 2025 project concluded, I recently wrote up a blog post https://beam.apache.org/blog/gsoc-25-ml-connectors/ about my experience working on the project in collaboration with the Beam community.
The work includes new connectors that enhance Beam's ML capabilities, particularly for RAG and feature engineering workflows: - **Milvus Vector Database**: Enrichment handler and sink I/O connector for vector similarity search operations - **Tecton Feature Store**: Enrichment handler and sink I/O connector for feature engineering workflows - **Embedding Generators**: OpenAI connector (with Anthropic coming soon) for generating vector embeddings These integrations expand Beam's ML ecosystem beyond the existing BigQuery, AlloyDB, Vertex AI, and Feast support. The project has already started attracting community contributions, including a new Qdrant vector database connector. These additions will be particularly valuable with Beam 3.0 coming up and its focus on first-class ML support. I'm excited to have contributed to making ML workflows more accessible in Beam, especially with the growing importance of RAG and feature engineering use cases. A huge thank you to my mentor Danny McCormick and the larger Beam community for your support throughout this project! Thanks, Mohamed Awnallah
