Load PostgreSQL data to Microsoft Fabric

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. Everything needed to build a working PostgreSQL → Microsoft Fabric 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.

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Build your PostgreSQL to Microsoft Fabric pipeline

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

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 PostgreSQL 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.

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PostgreSQL API at a glance

These values come from the PostgreSQL API reference — the authoritative source if anything here looks out of date.

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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

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[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 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.

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What PostgreSQL data can I load into Microsoft Fabric?

These are the PostgreSQL endpoints dlt can load into Microsoft Fabric:

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How do I load only new PostgreSQL records?

PostgreSQL exposes updated_at on {table_name}, 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.

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{"name": "tables", "endpoint": {
    "path": "{table_name}",
    "incremental": {"cursor_path": "updated_at", "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.

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What does the generated PostgreSQL pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading 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 Microsoft Fabric:

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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 load_postgresql_to_fabric() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="postgresql_pipeline",
        destination="fabric",
        dataset_name="postgresql_data",
    )
    load_info = pipeline.run(postgresql_source())
    print(load_info)


if __name__ == "__main__":
    load_postgresql_to_fabric()

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

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How do I query PostgreSQL data in Microsoft Fabric?

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):

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import dlt

data = dlt.pipeline("postgresql_pipeline").dataset()
df = data.tables.df()
print(df.head())

SQL:

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SELECT * FROM postgresql_data.tables LIMIT 10;

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

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How do I deploy the PostgreSQL to Microsoft Fabric 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 PostgreSQL loads into governed, documented models.
• Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →

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What other destinations can I load PostgreSQL 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.

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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