Databricks, written down.

Every concept explained in full on its own page, not a link to somebody else's tutorial. It is one body of theory you can enter three ways — from the product area you are already in, along a path, or against an exam guide — and an agent watches the release notes so the pages say what the product says today.

173

concepts

paths

596

resources

By product areaThe sidebar is the workspace panel: Catalog, Jobs & Pipelines, Compute, SQL, ML. Open the thing you were already looking at.Open the workspace →Along a path8 paths in stages, from SQL foundations to the platform. Each stage is a handful of concepts, in the order they make sense.See the paths →Against an exam guideFive certification roadmaps anchored to the official guides, with every domain pointing at the concept that covers it.See the certifications →

If you are starting from nothing

Lakehouse foundations, then whatever you need

What a lakehouse is, what Delta Lake does under it, and how Unity Catalog governs it — the three things every other page assumes you already know. After that the Data Engineer Associate roadmap is the most travelled route through the rest.

Start the foundations Data Engineer Associate Graph

New here? Start with

01Architecture of the Data Intelligence Platform 02Choosing compute: all-purpose, job cluster, serverless, SQL warehouse 03Delta Lake, the lakehouse table format

Latest news

All news

Tags for dashboards, notebooks, apps, and Genie Agents are GADatabricks release notes (AWS) · 25 Sept 2026Release Governed agentic app building is in betaDatabricks release notes (AWS) · 23 Sept 2026Preview Scala UDFs can now read Unity Catalog secretsDatabricks release notes (AWS) · 23 Sept 2026Release Unity Catalog Python UDFs are GA for scalar and batch useDatabricks release notes (AWS) · 23 Sept 2026Release Coding agents can be configured centrallyDatabricks release notes (AWS) · 22 Sept 2026Release

Recently updated

173 concepts

AI functions in SQLAI functions are built-in SQL functions, all named ai, that apply a model to a column. They run from the SQL editor, from notebooks, from Lakeflow pipelines and from jobs, and they need no endpoint of your own: the query runs on the compute you submit it…23 Sept 2026 ABAC policies in Unity CatalogABAC (attribute-based access control) is how Unity Catalog applies access rules based on the attributes of objects rather than their names. The attribute is a governed tag (pii = email, sensitivity = high); the rule is a policy attached to a metastore (Beta)…23 Sept 2026 Coding agents on Databricks, and how to keep them safeA coding agent — Claude Code, Cursor, GitHub Copilot, Codex — writes and runs code from a terminal or an editor. On its own it knows Databricks only as well as its training data does, which means old product names, flags that moved, and APIs that changed…23 Sept 2026 Evaluating agentsEvaluating an agent means running it against a set of representative inputs and scoring the outputs with scorers — some are LLM judges, some are plain code — instead of eyeballing transcripts. mlflow.genai.evaluate() is the entry point: give it an agent (or a…23 Sept 2026 Agent memoryManaged agent memory gives an agent something to remember between conversations. You create a memory store — a workspace-scoped container, addressed by its displayname — and Databricks provisions the Lakebase storage behind it for you.23 Sept 2026

Take it with you

Every concept is a markdown file

The whole site is written in a folder you can open in Obsidian: wikilinks resolve, the graph view works, callouts render. No plugins, nothing to configure.

Download the vault llms-full.txt, for a model