Load Firebase data to DuckDB

Firebase provides various REST APIs for interacting with services like Realtime Database, Cloud Firestore, and Authentication backend functionality. Everything needed to build a working Firebase → DuckDB pipeline is on this page: the API's base URL, authentication, endpoints, pagination and incremental field — plus a prompt that hands the whole job to your coding agent.

---

Build your Firebase to DuckDB pipeline

Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.

PromptCopy prompt
Run uvx dlthub-init@latest to build a pipeline from Firebase to DuckDB and run it on dltHub

That scaffolds a dltHub workspace and installs the dltHub AI harness — the project rules, the secrets-management skill, and the dlt MCP server your agent needs to work safely. From there it reads the Firebase API, proposes the endpoints to load, then writes, runs and validates the pipeline while you review rather than type. Credentials are inspected through MCP tools, so your agent never reads secrets.toml itself. How the LLM-native workflow works →

Prefer to write it yourself? Every fact the agent uses is below.

---

Firebase API at a glance

These values come from the Firebase API documentation. Check them against the vendor's current reference before relying on them in production.

---

How do I authenticate with the Firebase API?

The API uses the Authorization header with a Bearer token or an 'auth'/'access_token' query parameter. The Authorization header should be set as 'Authorization: Bearer '.

1. Get your credentials

To obtain Firebase API keys, log in to the Firebase Console, navigate to Project Settings (gear icon) > General tab. Under the 'Your apps' card, select your specific app to view its associated configuration details. Alternatively, to manage or create project-wide API keys, visit the Google Cloud Console, navigate to APIs & Services > Credentials, where you can view, create, or edit API keys for your Firebase project.

2. Add them to .dlt/secrets.toml

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

dlt reads this file automatically at runtime. With the harness, the setup-secrets skill prompts you for the values and never handles the raw credential in chat. For production, see setting up credentials with dlt.

---

What Firebase data can I load into DuckDB?

These are the Firebase endpoints dlt can load into DuckDB:

---

How do I load only new Firebase records?

Firebase exposes readTime on v1/projects/{projectId}/databases/{databaseId}/documents/{collectionId}, so dlt can request only the records that changed since the last run. Set it as the cursor_path and dlt tracks the high-water mark for you between runs.

Copy
{"name": "firestore_documents", "endpoint": {
    "path": "v1/projects/{projectId}/databases/{databaseId}/documents/{collectionId}",
    "data_selector": "documents",
    "incremental": {"cursor_path": "readTime", "initial_value": "2024-01-01T00:00:00Z"},
}}

On the first run dlt loads everything from initial_value; on every run after that it requests only what changed and appends with write_disposition="merge" if you set a primary key. See incremental loading.

---

What does the generated Firebase pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading v1.projects.databases.documents and v1.projects.databases.collectionGroups.indexes from the Firebase API into DuckDB:

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

@dlt.source
def firebase_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://firestore.googleapis.com (for Firestore) or https://firebasedatabase.googleapis.com (for Realtime Database Management)",
            "auth": {"type": "bearer", "token": access_token},
        },
        "resources": [
            {"name": "firestore_documents", "endpoint": {"path": "v1/projects/{projectId}/databases/{databaseId}/documents/{collectionId}", "data_selector": "documents"}},
            {"name": "firebase_projects", "endpoint": {"path": "v1beta1/projects", "data_selector": "projects"}}
        ],
    }
    yield from rest_api_resources(config)


def load_firebase_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="firebase_pipeline",
        destination="duckdb",
        dataset_name="firebase_data",
    )
    load_info = pipeline.run(firebase_source())
    print(load_info)


if __name__ == "__main__":
    load_firebase_to_duckdb()

Run it with python firebase_pipeline.py. The agent iterates on this until it loads cleanly — you review and approve, rather than write it from scratch.

---

How do I query Firebase data in DuckDB?

dlt creates one table per resource. Query the loaded data with Python or SQL — or ask your agent to, through the MCP server's execute_sql_query tool.

Python (pandas DataFrame):

Copy
import dlt

data = dlt.pipeline("firebase_pipeline").dataset()
df = data.firestore_documents.df()
print(df.head())

SQL:

Copy
SELECT * FROM firebase_data.firestore_documents LIMIT 10;

See querying your data with dataset and exploring it in marimo notebooks.

---

How do I deploy the Firebase to DuckDB pipeline in production?

The pipeline runs locally, which is ideal for prototyping and one-off analysis. When you need it on a schedule, monitored on every load, and shared with your team, deploy the same dlt code on the dltHub platform — no infrastructure to maintain. The prompt above already ends with "run it on dltHub", so your agent can take it there directly.

• Deploy & schedule — run the pipeline as a managed job with automatic retries.
• Monitor — observable job queues, alerting, and load metrics for every run.
• Transform — promote raw Firebase loads into governed, documented models.
• Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →

---

What other destinations can I load Firebase data to?

dlt loads into any of these — only the destination argument changes:

Set dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. On the dltHub platform the same pipeline runs against a managed Iceberg lakehouse. See the full destinations list.

---

Next steps

• Ready for production? Book a demo of the dltHub platform →
• dlt LLM-native workflow docs
• REST API source reference
• Join the dlt community on Slack