SQL

SQLFluff Core Rule Pack

• Configure SQLFluff for your warehouse dialect
• Customize rules for team standards

First hands-on — a correct SELECT with a justified join

Stand up a local DuckDB through the DuckDB MCP server, inspect before you query with list_tables and describe_table, and write one SELECT with a join whose cardinality you can justify.

Fix SQL lint with the SQLFluff core rule pack

Install the SQLFluff core rule pack, point it at your warehouse dialect, and lint a fixture that fails on purpose: indentation, keyword capitalisation, SELECT star, and unused CTEs.

Review SQL for fan-out joins

Follow the SQL Reviewer agent's four-step pass over a real query: read the diff and its context, establish the grain of every result set, apply the SQL Review skill's five rules while stopping at the first fan-out, and emit findings with file positions plus a rewritten clause you can verify against the twelve-case evaluation fixture.

Run the full SQL review pass on a PR

Run the SQL Reviewer agent's full pass over a pull request: read the diff in context, state the grain of every result set, then check the correctness bugs the SQL Review skill targets — fan-out joins, NULL-unsafe NOT IN and = predicates — and emit findings with file:line and a rewritten clause you can verify against the evaluation fixture.

The relational mental model — sets, grain, and NULL

Before the first SELECT, build the model every later lesson uses: a query returns a set with a stated grain, a join changes that grain by its cardinality, and NULL never compares as you expect.

Capstone — the SQL correctness checklist

Close the series by turning nine lessons into one standing checklist: a grain statement for every SELECT, a cardinality justification for every join, NULL-safe anti-joins, explicit casts, lint in CI, and one decision rule for slow queries.

Incident — the numbers doubled and no alarm fired

Row counts are inflated and metrics double-counted after a retry, a fan-out join or a backfill — the incident signature of a silent fan-out.

Production gotchas — NULL traps, implicit casts, silent drift

The bugs that survive testing compare wrong instead of failing loud: NOT IN over a nullable subquery, a comparison across types left to an implicit cast, and a schema change that silently turns a column to NULLs.

Read an EXPLAIN plan and pick the one fix

Take the EXPLAIN output of a slow query and read it bottom-up, naming the expensive node before you touch anything.

Window functions — keep the grain, rank the rows

A window function computes over a set of rows and hands the answer back to every row, so the result keeps its grain — the property that makes a keep-latest dedupe safe and a bare GROUP BY grain-changing.

SQL Review

A review pass that catches correctness bugs before the query reaches production.

SQL Query Plan Explainer

• Read a plan bottom-up and name the expensive node
• Detect cardinality misestimation from stale statistics

DuckDB / MotherDuck MCP Server

Queries DuckDB databases and MotherDuck, including reading Parquet/CSV directly from object storage.

SQL Reviewer

Review SQL before it merges, catching correctness bugs and fan-out joins.

DuckDB Documentation

DuckDB's documentation — the embedded analytics SQL engine, Parquet and CSV readers, in-process query execution, and extensions (ICU, JSON, Parquet, Excel). The fastest way to make a pipeline testable locally; covers everything our SQL review and testing skills reference, plus the DuckDB docs themselves.

Jev and DuckDB: Plain-English Conditions in SQL

TypeSafe AI released Jev, a model that returns typed answers instead of text, on September 15, 2026.

SQLBolt — Learn SQL with Simple Interactive Exercises

Free interactive SQL lessons: SELECT basics, filtering, joins, NULL handling and aggregate clauses, each runnable in the browser. A zero-setup first step before touching a real warehouse or local DuckDB.