Major Description
Apache Beam is a unified model for defining both batch and streaming data-parallel processing pipelines, as well as a set of language-specific SDKs for constructing pipelines and Runners for executing them on distributed processing backends. On top of providing lower level primitives, Beam has also introduced several higher level transforms used for machine learning and some general data processing use cases. This project focuses on identifying and implementing real world use cases that use these transforms
Objectives:
1. Add real world use cases demonstrating Beam's MLTransform for preprocessing data and generating embeddings
2. Add real world use cases demonstrating Beam's Enrichment transform for enriching existing data with data from a slowly changing source.
3. (Stretch) Implement 1 or more additional "enrichment handlers" for interacting with currently unsupported sources
Useful links:
Apache Beam repo - https://github.com/apache/beam
MLTransform docs - https://beam.apache.org/documentation/transforms/python/elementwise/mltransform/
Enrichment code - https://github.com/apache/beam/blob/master/sdks/python/apache_beam/transforms/enrichment.py
Enrichment docs (should be published soon) - https://github.com/apache/beam/pull/30187