The SQL hub for language reference, query performance and optimizer tuning, and worked exercises. Use the cards below to jump into syntax fundamentals, plan-reading, join strategy, or interview-style practice problems.
Language fundamentals across DDL, DML, DCL, and TCL, plus syntax basics and the logical SQL execution model.
Worked patterns for SELECT, INSERT, UPDATE, DELETE, and MERGE with concrete examples.
Reading EXPLAIN plans, index usage, table statistics, and hands-on techniques for tuning the optimizer.
Join algorithms (hash, merge, nested-loop), join order, and how to avoid accidental cartesian products.
Worked SQL exercise using WINDOW functions and grouped ranking to find top items per region.
Hands-on JOIN exercise across two tables, focused on key relationships and result shaping.
Text-to-SQL grounded in a YAML semantic model. The natural-language layer over a Snowflake warehouse, with a verified-query store for accuracy.
PySpark's pyspark.sql.functions over Parquet — the SQL-style API for distributed transformations on Databricks.
Pivot/unpivot patterns in PySpark SQL — the distributed equivalent of CASE WHEN aggregations on a single-node DB.
Serverless SQL over S3 using Presto/Trino. The default query engine for S3-based data lakes — no provisioning, pay-per-scan.
Schema-design fundamentals: normalization vs. denormalization, naming conventions, and how schema choice drives query performance.
1NF through BCNF — when to normalize for integrity and when to denormalize for analytical query speed.
About this section. The first four cards form the language and optimizer reference: SQL Overview and SQL Statements cover the syntax and statement patterns, while Query Performance and Optimizing Joins cover plan-reading and tuning. The last two cards are interview-style practice problems — short, self-contained exercises you can run end-to-end to drill window functions, grouped ranking, and multi-table joins.