Load Asana data to DuckDB

Asana is a work management platform that provides a REST API for programmatic access to tasks, projects, and workspace data. Everything needed to build a working Asana → 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.

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Build your Asana 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 Asana 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 Asana 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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Asana API at a glance

These values come from the Asana API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Asana API?

All requests to the Asana API require an Authorization header using the Bearer token scheme. The header should be formatted as: Authorization: Bearer <your_token>.

1. Get your credentials

To obtain a Personal Access Token (PAT) for the Asana REST API: 1. Log in to your Asana account. 2. Click your profile photo in the top right corner and select 'My Settings'. 3. Navigate to the 'Apps' tab. 4. Click 'Manage Developer Apps' (or 'View developer console' if applicable). 5. Under the 'Personal access tokens' section, click 'Create new token'. 6. Provide a name or description for the token, agree to the API terms, and click 'Create token'. 7. Copy the token immediately; it will not be displayed again.

2. Add them to .dlt/secrets.toml

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[sources.asana_source]
api_key = "your_personal_access_token_here"

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 Asana data can I load into DuckDB?

These are the Asana endpoints dlt can load into DuckDB:

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How do I load only new Asana records?

Asana exposes modified_at on tasks, 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": "tasks", "endpoint": {
    "path": "tasks",
    "data_selector": "data",
    "incremental": {"cursor_path": "modified_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 Asana pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading users/me and tasks from the Asana API into DuckDB:

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

@dlt.source
def asana_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://app.asana.com/api/1.0",
            "auth": {"type": "bearer", "token": access_token},
        },
        "resources": [
            {"name": "tasks", "endpoint": {"path": "tasks", "data_selector": "data"}},
            {"name": "projects", "endpoint": {"path": "projects", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def load_asana_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="asana_pipeline",
        destination="duckdb",
        dataset_name="asana_data",
    )
    load_info = pipeline.run(asana_source())
    print(load_info)


if __name__ == "__main__":
    load_asana_to_duckdb()

Run it with python asana_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 Asana 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):

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import dlt

data = dlt.pipeline("asana_pipeline").dataset()
df = data.tasks.df()
print(df.head())

SQL:

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SELECT * FROM asana_data.tasks LIMIT 10;

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

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How do I deploy the Asana 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 Asana 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 Asana 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