Investigate data architectures and approaches for integrating siloed data with an emphasis on tooling and implementation of end-to-end solutions. Explore data sourcing, storage, ingestion, modeling, transformations, enrichment, aggregation, lineage, metadata, quality, and knowledge graphs. Three lecture hours a week for one semester. Computer Science 329E (Topic: Elements of Data Integration) and 333E may not both be counted. Prerequisite: Computer Science 327E with a grade of at least C-.