Load Planview Community data to DuckDB

Planview Community API is a REST interface for accessing AdaptiveWork and Portfolios data. Everything needed to build a working Planview Community → 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 Planview Community 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 Planview Community 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 Planview Community 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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Planview Community API at a glance

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

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How do I authenticate with the Planview Community API?

Authentication is performed by including a long-lived API token in the Authorization header using the Bearer scheme, e.g., 'Authorization: Bearer '.

1. Get your credentials

1. Log in to the Planview Success Center with your account. 2. Navigate to Administration → API Access. 3. Click Create New Token, give it a name, and set the required scopes. 4. Copy the generated token immediately, as it will be shown only once. 5. Store the token securely to use in your dlt pipeline configuration.

2. Add them to .dlt/secrets.toml

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[sources.planview_community_source]
api_key = "your_api_key_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 Planview Community data can I load into DuckDB?

These are the Planview Community endpoints dlt can load into DuckDB:

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How do I load only new Planview Community records?

Planview Community exposes project.Id on work?filter=project.Id .eq {projectId}, 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": "work_for_project", "endpoint": {
    "path": "work?filter=project.Id .eq {projectId}",
    "data_selector": "Data",
    "incremental": {"cursor_path": "project.Id", "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 Planview Community pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading public-api/v1/work and v2.0/services/data/query from the Planview Community API into DuckDB:

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

@dlt.source
def planview_community_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://apie1.clarizen.com",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "work_for_project", "endpoint": {"path": "work?filter=project.Id .eq {projectId}", "data_selector": "Data"}},
            {"name": "work", "endpoint": {"path": "public-api/v1/work"}}
        ],
    }
    yield from rest_api_resources(config)


def load_planview_community_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="planview_community_pipeline",
        destination="duckdb",
        dataset_name="planview_community_data",
    )
    load_info = pipeline.run(planview_community_source())
    print(load_info)


if __name__ == "__main__":
    load_planview_community_to_duckdb()

Run it with python planview_community_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 Planview Community 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("planview_community_pipeline").dataset()
df = data.work.df()
print(df.head())

SQL:

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SELECT * FROM planview_community_data.work LIMIT 10;

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

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How do I deploy the Planview Community 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 Planview Community 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 Planview Community 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