Product area
Workspace
The Databricks workspace: platform architecture, notebooks, files, Git folders, bundles, and the development workflow.
Concepts
- 1Architecture of the Data Intelligence PlatformThe Data Intelligence Platform is the name Databricks gives to the set of services it offers on top of a public cloud (AWS, Azure, GCP). The underlying idea is the lakehouse: data stays in cheap, open object storage, but on top of it you get transactions…Beginner
- 2NotebooksA notebook is the default place to write and run code in the workspace: an ordered list of cells, each holding either code or formatted text, that you execute one at a time or all together against attached compute. One notebook can carry Python, SQL, Scala…Beginner
- 3Workspace files and volumesFour different places end up holding "files" (as opposed to tables) on Databricks, and it's easy to reach for the wrong one:Beginner
- 4Secrets and credentialsA secret is a key-value pair stored outside your notebooks and job definitions, so a database password or an API key never appears as a literal string in code that gets committed, shared, or logged. Secrets live inside a secret scope, a named container you…Intermediate
- 5Git folders: branches, commits, pull requestsA Git folder is a folder in the workspace that is also a clone of a remote Git repository. Inside it you work as in any other folder (notebooks, .py, .sql, YAML files), but on top of that you get a visual Git client: branch, commit, push, pull, merge, rebase…Beginner
- 6The CLI and the SDKsThe Databricks CLI is a single binary that turns every workspace and account operation into a terminal command; the Python SDK (databricks-sdk) exposes the same operations as a typed WorkspaceClient object for use inside scripts; Databricks Connect goes one…Intermediate
- 7Declarative Automation Bundles and the Databricks CLIA bundle is a project that keeps, in a single Git-versioned folder, both the code (notebooks, Python files, SQL, wheels) and the declarative definition of the Databricks resources that run it: Lakeflow Jobs, Lakeflow pipelines, dashboards, MLflow experiments…Intermediate
- 8Bundles: variables, targets, and per-environment overridesIn a bundle (see bundles overview), variables are named values that the YAML references with ${var.name}; targets are the deployment environments (dev, test, prod), each with its own workspace, its own mode, and the ability to override variables and pieces of…Intermediate
- 9Bundles in a CI/CD pipelineThis is a Declarative Automation Bundle, the project described in bundles overview, driven by a build server instead of by a person at a terminal. Databricks documents a four-stage flow for it: compile and test the code, upload the compiled file under a…Intermediate
- 10Coding 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…Intermediate
- 11Cost attribution and budgetsCost attribution is the practice of making every DBU answerable to a team, a project or a cost centre. On Databricks it is three mechanisms, and they are not interchangeable. Tags put a key:value label on a compute resource, and that label follows the usage…Intermediate
Also covered here
- Genie CodeGenie Code is the assistant built into the workspace. It writes and runs code, builds pipelines and dashboards, explains and fixes errors, and reads Unity Catalog to know what your tables actually contain.
- System tablesSystem tables are read-only, Databricks-hosted tables in a catalog called system that record how your account is actually used: what it costs, which jobs ran and how they ended, who read which table, which clusters existed, which statements executed. You…
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