Load Trello data to Microsoft SQL Server

Trello is a collaboration and project-management service whose REST API provides access to boards, lists, cards, members, and related activity. Everything needed to build a working Trello → Microsoft SQL Server 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 Trello to Microsoft SQL Server 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 Trello 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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Trello API at a glance

These values come from the Trello API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Trello API?

Trello API key and user token authentication is supplied as query parameters on requests: ?key={apiKey}&token={apiToken}. The API also documents an OAuth Authorization header form, Authorization: OAuth oauth_consumer_key="{apiKey}", oauth_token="{apiToken}"; no additional required request header is stated for key+token query authentication.

1. Get your credentials

1. Create a Trello Power-Up. 2. Open https://trello.com/power-ups/admin (the older documentation also refers to https://trello.com/apps/admin). 3. Select the Power-Up and open Authorization. 4. Select Trello auth and choose Generate a new API Key; copy the API Key, not the secret. 5. Select the Token link beside the key, authorize the requested read access, and copy the token shown after approval. 6. Store both values securely and send them with API requests as query parameters key=<API_KEY>&token= (or use Trello's OAuth-style Authorization header). For a personal token, the documented authorization URL is https://trello.com/1/authorize?expiration=1day&scope=read&response_type=token&key=<API_KEY>; use an appropriate expiration/scope for production.

2. Add them to .dlt/secrets.toml

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[sources.trello_source]
api_key = "your_trello_api_key"
token = "your_trello_token"

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 Trello data can I load into Microsoft SQL Server?

These are the Trello endpoints dlt can load into Microsoft SQL Server:

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How do I load only new Trello records?

Trello exposes dateLastActivity on boards/{id}/cards, 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": "board_cards", "endpoint": {
    "path": "boards/{id}/cards",
    "incremental": {"cursor_path": "dateLastActivity", "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 Trello pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading GET /1/members/{memberId}/boards and GET /1/boards/{boardId}/cards from the Trello API into Microsoft SQL Server:

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

@dlt.source
def trello_source(api_key_and_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.trello.com/1",
            "auth": {"type": "bearer", "token": api_key_and_token},
        },
        "resources": [
            {"name": "board_cards", "endpoint": {"path": "boards/{id}/cards"}},
            {"name": "card", "endpoint": {"path": "cards/{id}"}}
        ],
    }
    yield from rest_api_resources(config)


def load_trello_to_mssql() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="trello_pipeline",
        destination="mssql",
        dataset_name="trello_data",
    )
    load_info = pipeline.run(trello_source())
    print(load_info)


if __name__ == "__main__":
    load_trello_to_mssql()

Run it with uv run python trello_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 Trello data in Microsoft SQL Server?

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("trello_pipeline").dataset()
df = data.board_cards.df()
print(df.head())

SQL:

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SELECT * FROM trello_data.board_cards LIMIT 10;

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

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How do I deploy the Trello to Microsoft SQL Server 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 Trello 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 Trello 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