Lilly Tech Systems
Data Catalog & Discovery
Data catalogs are the search engines of your data platform. Learn how to implement metadata management, search, data lineage, and discovery tools that help AI teams find, understand, and trust the data they need.
Your Learning Path
Follow these lessons to master data cataloging and discovery for AI.
1. Introduction
What is a data catalog, why it matters for AI, and the data discovery challenge.
2. Metadata Management
Technical, business, and operational metadata. Collection, storage, and enrichment strategies.
3. Search & Discovery
Building searchable catalogs with semantic search, recommendations, and AI-powered discovery.
4. Data Lineage
Track data from source to consumption. Column-level lineage, impact analysis, and debugging.
5. Tools
Deep dive into Amundsen, DataHub, OpenMetadata, and commercial catalog platforms.
6. Best Practices
Adoption strategies, governance integration, and making your catalog indispensable.
What You'll Learn
By the end of this course, you'll be able to:
Manage Metadata
Collect, organize, and enrich metadata across your entire data ecosystem.
Enable Discovery
Build search experiences that help AI teams find relevant data in minutes.
Track Lineage
Trace data from source to model, enabling impact analysis and debugging.
Choose Tools
Evaluate and implement the right data catalog platform for your needs.