Learning paths
A path is an ordered map of concepts: stages you can follow front to back, or a graph you can jump into wherever you already know the ground. Progress is kept in your browser.
The SQL and PySpark you need before any other path: the Spark dialect, types, joins, windows, MERGE, and the DataFrame API as it differs from pandas.
How the Databricks platform is put together: control plane and compute, Delta Lake as the storage format, Unity Catalog as the governance layer, and the medallion pattern everything else builds on.
Build production pipelines: ingest with Auto Loader, COPY INTO and Lakeflow Connect, transform with PySpark and SQL, orchestrate with Lakeflow Jobs, and ship it with bundles.
Unity Catalog end to end: the three-level namespace, managed and external tables, privileges, row filters, column masks and attribute-based policies.
Choose the right compute, read the Spark UI, tune the handful of settings that matter, and diagnose clusters that will not start or run out of memory.
Query the lakehouse from the SQL editor, model gold tables and views for BI, and understand what a SQL warehouse costs.
Track experiments with MLflow, build features, register models in Unity Catalog and serve them behind an endpoint.
Foundation models on Databricks, vector search for retrieval, agents built with Agent Bricks and the Agent Framework, and how to evaluate them.