The 5 SQL Patterns Every Data Analyst Must Know Copy

1. Window Functions with PARTITION BY

Window functions are one of SQL's most powerful features and among the most underused. They let you perform aggregations without collapsing rows — you preserve row-level detail while computing group-level metrics simultaneously.

"Window functions are where SQL stops feeling like a toy and starts feeling like a real analytical tool."

2. Recursive CTEs for Hierarchical Data

Most data has some form of hierarchy. Common use cases:

• Traversing org hierarchies to find all reports under a manager

• Calculating depth in category trees

• Building date spine tables for gap analysis

3. The CASE WHEN Pivot

Pivoting data — turning row values into columns — comes up often. Use CASE WHEN inside an aggregate function to selectively include values for each desired column.

4. Self-Joins for Sequential Analysis

Sometimes you need to compare a row to other rows in the same table. Self-joins are the cleanest approach for finding consecutive event pairs — previous session, next purchase, adjacent day.

5. Conditional Aggregation for Multi-Metric Reports

Rather than running multiple queries, skilled analysts combine all metrics into a single aggregation using conditional logic. This reduces query time and complexity dramatically.