Databricks Python API Docs | dltHub

Databricks is a unified data analytics and Lakehouse platform that provides REST APIs for managing workspace and account resources. The REST API base URL is Workspace: https://<databricks-instance> (e.g., https://adb-123456789012345.18.azuredatabricks.net) ; Account: https://accounts.cloud.databricks.com and All requests require a Bearer token (OAuth or Personal Access Token) in the Authorization header..

dlt is an open-source Python library that handles authentication, pagination, and schema evolution automatically. dlthub provides AI context files that enable code assistants to generate production-ready pipelines. Install with uv pip install "dlt[workspace]" and start loading Databricks data in under 10 minutes.

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What data can I load from Databricks?

Here are some of the endpoints you can load from Databricks:

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How do I authenticate with the Databricks API?

Use OAuth or a Personal Access Token and include it as a Bearer token in the Authorization header (Authorization: Bearer ). Workspace requests go to your workspace host; account requests use the accounts.cloud.databricks.com host and require the account_id path segment.

1. Get your credentials

1. Sign in to your Databricks workspace. 2) Click the user icon (top‑right) and choose User Settings (or Admin Console for service principals). 3) In the Access Tokens section, click Generate New Token. 4) Provide a comment and optional lifetime, then click Generate. 5) Copy the generated token and store it securely; it will not be shown again.

2. Add them to .dlt/secrets.toml

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[sources.databricks_source]
token = "your_databricks_pat_here"

dlt reads this automatically at runtime — never hardcode tokens in your pipeline script. For production environments, see setting up credentials with dlt for environment variable and vault-based options.

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How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

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dlt ai init --agent <your-agent> # <agent>: claude | cursor | codex

This installs project rules, a secrets management skill, appropriate ignore files, and configures the dlt MCP server for your agent. Learn more →

2. Install the rest-api-pipeline toolkit:

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dlt ai toolkit rest-api-pipeline install

This loads the skills and context about dlt the agent uses to build the pipeline iteratively, efficiently, and safely. The agent uses MCP tools to inspect credentials — it never needs to read your secrets.toml directly. Learn more →

3. Start LLM-assisted coding:

Use /find-source to load data from the Databricks API into DuckDB.

The rest-api-pipeline toolkit takes over from here — it reads relevant API documentation, presents you with options for which endpoints to load, and follows a structured workflow to scaffold, debug, and validate the pipeline step by step.

4. Run the pipeline:

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python databricks_pipeline.py

If everything is configured correctly, you'll see output like this:

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Pipeline databricks_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset databricks_data
The duckdb destination used duckdb:/databricks.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

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dlt pipeline databricks_pipeline show

This opens the Pipeline Dashboard where you can verify pipeline state, load metrics, schema (tables, columns, types), and query the loaded data directly.

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Python pipeline example

This example loads clusters and jobs_runs from the Databricks API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def databricks_source(token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "Workspace: https://<databricks-instance> (e.g., https://adb-123456789012345.18.azuredatabricks.net) ; Account: https://accounts.cloud.databricks.com",
            "auth": {
                "type": "bearer",
                "token": token,
            },
        },
        "resources": [
            {"name": "clusters", "endpoint": {"path": "api/2.1/clusters/list", "data_selector": "clusters"}},
            {"name": "jobs_runs", "endpoint": {"path": "api/2.0/jobs/runs/list", "data_selector": "runs"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="databricks_pipeline",
        destination="duckdb",
        dataset_name="databricks_data",
    )
    load_info = pipeline.run(databricks_source())
    print(load_info)

To add more endpoints, append entries from the resource table to the "resources" list using the same name, path, and data_selector pattern.

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How do I query the loaded data?

Once the pipeline runs, dlt creates one table per resource. You can query with Python or SQL.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("databricks_pipeline").dataset()
sessions_df = data.clusters.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM databricks_data.clusters LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("databricks_pipeline").dataset()
data.clusters.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

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What destinations can I load Databricks data to?

dlt supports loading into any of these destinations — only the destination parameter changes:

Change the destination in dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. See the full destinations list.

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Troubleshooting

Authentication failures

If requests return 401 Unauthorized or 403 Forbidden: verify you are using a valid PAT or OAuth token, that it has not expired, and that the token has appropriate scopes/permissions. Ensure Authorization: Bearer <token> header is sent and the request is addressed to the correct workspace host (your workspace URL).

Rate limits

Databricks enforces per‑endpoint and per‑workspace rate limits. Exceeding limits returns 429 Too Many Requests. Implement exponential backoff and retry for 429 responses and respect next_page_token pagination rather than retrieving large pages in tight loops.

Pagination and data selectors

Many listing endpoints return arrays inside top‑level fields and include next_page_token for pagination. Use the documented data selector keys: clusters (clusters/list), runs (jobs/runs/list), objects (workspace/list), files (dbfs/list), versions (clusters/spark-versions). If next_page_token is present, include it in subsequent requests to retrieve additional pages.

Common error formats

Error responses are JSON objects:

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{ "error_code": "Error code", "message": "Human‑readable error message." }

Endpoints may return HTTP 400, 401, 403, 429, 500.

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

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Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI Workbench:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-runtime — Deploy, schedule, and monitor your pipeline in production.

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dlt ai toolkit data-exploration install
dlt ai toolkit dlthub-runtime install

• How to deploy a pipeline
• How to explore your data in marimo Notebooks
• How to query your data in Python with dataset
• Join the dlt community on Slack