Metabase Python API Docs | dltHub

Metabase is an open-source business intelligence platform that exposes a REST API for automating Metabase administration, managing databases, collections, cards (questions), dashboards, users, and executing queries. The REST API base URL is https://<your-metabase-host>/api and All requests require authentication via either an API key (recommended) or a session token (X-Metabase-Session)..

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 Metabase data in under 10 minutes.

---

What data can I load from Metabase?

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

---

How do I authenticate with the Metabase API?

Metabase supports two primary auth mechanisms: API keys (via the Authorization header: "Bearer <api_key>") and session tokens obtained by POST /api/session which must be included in requests as the header X-Metabase-Session: <session_id>. All requests and bodies are JSON (Content-Type: application/json).

1. Get your credentials

1. Log in to your Metabase web UI as an admin. 2. Go to Admin settings → Authentication or Admin → API Keys (depending on Metabase version) or visit /admin/settings or /api/api-key in API docs. 3. Create a new API key (or note that earlier versions only support session tokens via POST /api/session with username/password). 4. Copy the generated API key and store securely; use it in the Authorization header as Bearer <api_key>. (If API keys are not available, use POST /api/session with {"username":..., "password":...} to obtain {"id": "<session_id>"} then set X-Metabase-Session header.)

2. Add them to .dlt/secrets.toml

Copy
[sources.metabase_instance_source]
api_key = "your_api_key_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.

---

How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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

1. Install the dlt AI Workbench:

Copy
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:

Copy
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 Metabase 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:

Copy
python metabase_instance_pipeline.py

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

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

Inspect your pipeline and data:

Copy
dlt pipeline metabase_instance_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.

---

Python pipeline example

This example loads databases and cards from the Metabase API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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

@dlt.source
def metabase_instance_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://<your-metabase-host>/api",
            "auth": {
                "type": "bearer",
                "token": api_key,
            },
        },
        "resources": [
            {"name": "databases", "endpoint": {"path": "database"}},
            {"name": "cards", "endpoint": {"path": "card"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="metabase_instance_pipeline",
        destination="duckdb",
        dataset_name="metabase_instance_data",
    )
    load_info = pipeline.run(metabase_instance_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.

---

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):

Copy
import dlt

data = dlt.pipeline("metabase_instance_pipeline").dataset()
sessions_df = data.databases.df()
print(sessions_df.head())

SQL (DuckDB example):

Copy
SELECT * FROM metabase_instance_data.databases LIMIT 10;

In a marimo or Jupyter notebook:

Copy
import dlt

data = dlt.pipeline("metabase_instance_pipeline").dataset()
data.databases.df().head()

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

---

What destinations can I load Metabase 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.

---

Troubleshooting

common API errors / troubleshooting (brief)

• 401 Unauthorized: Invalid or expired X-Metabase-Session token or missing/invalid API key. Remedy: re-authenticate (POST /api/session) or use valid API key (Authorization: Bearer ). Sessions are rate-limited; cache tokens.
• 403 Forbidden: Endpoint requires admin/superuser privileges.
• 404 Not Found: Invalid endpoint or resource id.
• 429 Too Many Requests: Login/session creation endpoints are rate-limited; implement retry/backoff.
• 500/5xx: Server error; check Metabase logs.

Caveats and behavior

• The API is not versioned; endpoints can change across Metabase releases.
• Live API docs available at https:///api/docs; useful to inspect exact response schemas per your Metabase version.
• Many endpoints return top-level arrays; verify by inspecting your instance’s /api/docs or the response via browser devtools.

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

---

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.

Copy
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