Firebase Python API Docs | dltHub

Firebase is a suite of backend services (Realtime Database, Firestore, Authentication, Cloud Messaging, etc.) that provide hosted NoSQL databases, messaging, and auth services exposed with REST endpoints. The REST API base URL is https://firebase.googleapis.com and Auth varies by service: Realtime Database accepts access_token or auth query parameters; Firestore and FCM use OAuth 2.0 Bearer tokens..

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 Firebase data in under 10 minutes.

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What data can I load from Firebase?

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

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How do I authenticate with the Firebase API?

Realtime Database can be accessed anonymously or with a query‑string token (auth=FIREBASE_SECRET or access_token=OAUTH_TOKEN). Firestore and FCM require an Authorization: Bearer <access_token> header obtained via a Google service‑account or OAuth flow.

1. Get your credentials

1. Open Firebase Console > Project Settings > Service Accounts. 2. For Realtime Database legacy projects, locate Database secrets (optional). 3. Click “Generate new private key” to download a service‑account JSON file. 4. Use the JSON with google‑auth libraries or run gcloud auth application-default login to obtain an OAuth2 access token with the required scopes (e.g., https://www.googleapis.com/auth/datastore or https://www.googleapis.com/auth/firebase.messaging). 5. For client‑side requests, retrieve a Firebase Authentication ID token from the signed‑in user and use it as a Bearer token where supported.

2. Add them to .dlt/secrets.toml

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[sources.firebase_source]
service_account_key = "{...contents of service account JSON...}"
oauth_access_token = "ya29.."
firebase_database_url = "https://your-db.firebaseio.com"

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 Firebase 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 firebase_pipeline.py

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

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Pipeline firebase_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset firebase_data
The duckdb destination used duckdb:/firebase.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 firebase_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 documents and realtime_nodes from the Firebase 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 firebase_source(service_account_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://firebase.googleapis.com",
            "auth": {
                "type": "bearer",
                "token": service_account_key,
            },
        },
        "resources": [
            {"name": "documents", "endpoint": {"path": "v1/projects/[PROJECT_ID]/databases/(default)/documents"}},
            {"name": "realtime_nodes", "endpoint": {"path": "[PROJECT_ID].firebaseio.com/.json"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="firebase_pipeline",
        destination="duckdb",
        dataset_name="firebase_data",
    )
    load_info = pipeline.run(firebase_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("firebase_pipeline").dataset()
sessions_df = data.documents.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM firebase_data.documents LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("firebase_pipeline").dataset()
data.documents.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 Firebase 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

• Firestore/FCM: 401 Unauthorized if OAuth2 token is missing/expired. Ensure Authorization: Bearer <access_token> with token obtained from service account or OAuth flow. For FCM use scope https://www.googleapis.com/auth/firebase.messaging.
• Realtime Database: 401 or permission denied in response when rules block access. Legacy projects may use auth=FIREBASE_SECRET or access_token query param; prefer OAuth tokens.

Rate limits and quota

• Google APIs enforce per-project quotas and per-method limits; Firestore and FCM will return 429 Too Many Requests when throttled. Check Google Cloud Console Quotas and enable billing if needed.

Pagination and large responses

• Firestore ListDocuments responses include nextPageToken for pagination; include pageSize and pageToken query params to page through results.
• Realtime Database supports shallow=true to avoid downloading deep trees and supports timeout and writeSizeLimit to limit operations; use streaming for incremental updates.

Common errors

• 400 Bad Request: malformed JSON, unsupported query params, request too large (Realtime Database WRITE_TOO_BIG).
• 401 Unauthorized: missing/invalid token.
• 403 Forbidden: permission denied by security rules or insufficient OAuth scopes.
• 404 Not Found: incorrect project or path.
• 412 Precondition Failed: ETag mismatch for conditional writes.
• 429 Too Many Requests: quota exceeded.

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