PostgreSQL Python API Docs | dltHub

PostgREST is a standalone web server that turns a PostgreSQL database directly into a RESTful API by mapping database schema and permissions to HTTP endpoints. The REST API base URL is https://<PROJECT_REF>.supabase.co/rest/v1/ and all requests require a Bearer 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 add "dlt[hub]" and start loading PostgreSQL data in under 10 minutes.

---

What data can I load from PostgreSQL?

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

---

How do I authenticate with the PostgreSQL API?

Authentication is handled via the Authorization HTTP header using the Bearer scheme, typically containing a JSON Web Token (JWT). The token is expected to be a cryptographically signed JWT.

1. Get your credentials

PostgreSQL does not have a native REST API. If you are using PostgREST to expose your database as an API, credentials are managed via a JWT (JSON Web Token) secret. To set this up: 1) Define a 32+ character string as your 'jwt-secret' in your PostgREST configuration file (e.g., 'tutorial.conf'). 2) To authenticate requests, generate a JWT (often using an external service or internal database function) signed with this secret. 3) Pass this token in your HTTP requests using the 'Authorization: Bearer ' header. There is no standard 'dashboard' for this; it is configured via the server's configuration file or environment variables (e.g., 'PGRST_JWT_SECRET').

2. Add them to .dlt/secrets.toml

Copy
[sources.postgresql_source]
# If you are configuring a dlt destination to talk to a Postgres database directly (not via a REST API):
database = "dlt_data"
username = "loader"
password = "your_password_here"
host = "localhost"
port = 5432

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 init
uv add "dlt[hub]"

1. Install the dlt AI harness:

Copy
uv run dlthub 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
uv run dlthub ai toolkit install rest-api-pipeline

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 PostgreSQL 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
uv run python postgresql_pipeline.py

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

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

Inspect your pipeline and data:

Copy
uv run dlthub 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 GET /databases and POST /databases/{database_name}/query (Note: These refer to generic third-party PostgreSQL REST API services; native PostgREST endpoints are dynamically generated based on your database schema, e.g., /your_table_name). from the PostgreSQL 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 postgresql_source(token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://<PROJECT_REF>.supabase.co/rest/v1/",
            "auth": {"type": "bearer", "token": token},
        },
        "resources": [
            {"name": "tables", "endpoint": {"path": "{table_name}"}},
            {"name": "views", "endpoint": {"path": "{view_name}"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="postgresql_pipeline",
        destination="duckdb",
        dataset_name="postgresql_data",
    )
    load_info = pipeline.run(postgresql_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("postgresql_pipeline").dataset()
sessions_df = data.tables.df()
print(sessions_df.head())

SQL (DuckDB example):

Copy
SELECT * FROM postgresql_data.tables LIMIT 10;

In a marimo or Jupyter notebook:

Copy
import dlt

data = dlt.pipeline("postgresql_pipeline").dataset()
data.tables.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 PostgreSQL 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.

---

Next steps

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

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

Copy
uv run dlthub ai toolkit install data-exploration
uv run dlthub ai toolkit install dlthub-platform

• 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