Module 06
Warehouses & Lakehouses
Use modern warehouses and lakehouse technologies to store, query, optimize, and govern data for AI systems.
7 lessons · 9 videos · 1h 51m- 06.01
Warehouse vs Lakehouse Tradeoffs
Choose between a cloud warehouse, lakehouse, or hybrid architecture for specific ML and analytics workloads.
- 06.02
Snowflake for ML Data
Create tables, load data, run transformations, and manage compute warehouses for an ML training dataset in Snowflake.
- 06.03
BigQuery for Large-Scale Analytics
Partition, cluster, query, and cost-estimate a large ML feature table in BigQuery.
- 06.04
Databricks and Unified Analytics
Use Databricks notebooks and Delta tables to build an offline feature transformation pipeline.
- 06.05
Parquet and Columnar Storage
Explain how columnar storage, compression, predicate pushdown, and row groups affect query speed and ML data loading.
- 06.06
Iceberg, Delta Lake, and Table Formats
Compare Iceberg and Delta Lake features including ACID transactions, schema evolution, partition evolution, and time travel.
- 06.07
Catalogs, Lineage, and Access Control
Configure a conceptual data catalog entry with ownership, schema metadata, lineage, tags, and access policies.