RANK, LAG, and rolling aggregates — the SQL that turns row-by-row data into answers about change over time.
Facts, dimensions, and the grain — how to shape tables so every later query gets simpler.
A full transformation pipeline on your laptop: dbt models compiled straight into an in-process DuckDB.
From raw files to answers with dataframes — the working core of analytical Python.
Freshness, volume, and validity checks — catch broken data before your stakeholders do.