Phase 2: Enterprise Data Engineering · 55 min · Python · Snowflake · BigQuery

Advanced SQL for Client Databases

Your SQL is the bridge between the client's data and your AI system.

What you will learn

Write window functions: PARTITION BY, ORDER BY, frame clauses, running totals, moving averages
Choose between CTEs, subqueries, and temp tables based on readability and performance
Use Snowflake-specific features: VARIANT, FLATTEN, lateral joins, time travel
Use BigQuery-specific features: ARRAY, STRUCT, UNNEST, partition pruning, clustering keys
Optimize queries using EXPLAIN ANALYZE, identifying full table scans and indexing strategies

Objective

What you'll learn

This lesson takes approximately 55 min. By the end, you will be able to:

Write window functions: PARTITION BY, ORDER BY, frame clauses, running totals, moving averages
Choose between CTEs, subqueries, and temp tables based on readability and performance
Use Snowflake-specific features: VARIANT, FLATTEN, lateral joins, time travel
Use BigQuery-specific features: ARRAY, STRUCT, UNNEST, partition pruning, clustering keys
Optimize queries using EXPLAIN ANALYZE, identifying full table scans and indexing strategies

The Problem

Enterprise databases are not toy datasets. They have millions of rows, complex joins across dozens of tables, window functions for time-series analysis, and query plans that need optimization. Your RAG system's retrieval quality depends on getting the SQL right — a poorly written query that returns wrong rows means the LLM generates confident but wrong answers.

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers The Concept, Build, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.

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