Summary
You've learned how to design and implement data modeling strategies that form the foundation of a well-architected Azure Databricks lakehouse. Starting with ingestion logic design, you explored how to match extraction types to source system capabilities and latency requirements. You evaluated ingestion tools—from Lakeflow Connect for managed connectors to Auto Loader for file-based streaming—and selected the right tool for each scenario.
Table format selection between Delta Lake and Apache Iceberg depends on your requirements: Delta Lake for deep Azure Databricks integration and Apache Iceberg when cross-platform compatibility is essential. You designed partitioning schemes that enable partition pruning for tables exceeding 1 TB. Slowly changing dimension types give you the tools to maintain historical accuracy where business requirements demand it.
Granularity decisions establish analytical flexibility. Starting with atomic grain preserves maximum detail, while aggregate tables optimize common query patterns. Liquid clustering organizes data for efficient data skipping, and managed tables simplify operations through predictive optimization.
Apply these concepts by evaluating your current data pipelines against the decision frameworks presented. Start with one table that would benefit from improved clustering, measure performance before and after, and expand your optimizations based on results.