Load Planview ProjectPlace data to Microsoft Fabric

Planview ProjectPlace provides a REST API for accessing project management, collaboration, and work data within the ProjectPlace platform. Everything needed to build a working Planview ProjectPlace → 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.

---

Build your Planview ProjectPlace 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 Planview ProjectPlace 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.

---

Planview ProjectPlace API at a glance

These values come from the Planview ProjectPlace API reference — the authoritative source if anything here looks out of date.

---

How do I authenticate with the Planview ProjectPlace API?

Authentication is handled via OAuth2. Access tokens are provided in the Authorization header as a Bearer token.

1. Get your credentials

To obtain credentials for the Planview ProjectPlace API, you must register your application via the ProjectPlace developer portal. After creating an app, you will be provided with a Client ID (or Application Key) and a Client Secret. Depending on your integration, you may also need a Redirect URI. Ensure these credentials are kept secure and are not hard-coded in public repositories.

2. Add them to .dlt/secrets.toml

Copy
[sources.planview_projectplace_source]
projectplace_client_id = "your_client_id_here"
projectplace_client_secret = "your_client_secret_here"
projectplace_subdomain = "your_subdomain"

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.

---

What Planview ProjectPlace data can I load into Microsoft Fabric?

These are the Planview ProjectPlace endpoints dlt can load into Microsoft Fabric:

---

How do I load only new Planview ProjectPlace records?

Planview ProjectPlace exposes iterator on 2/account/projects, 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.

Copy
{"name": "workspaces", "endpoint": {
    "path": "2/account/projects",
    "incremental": {"cursor_path": "iterator", "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.

---

What does the generated Planview ProjectPlace pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /oauth2/authorize and /oauth2/access_token from the Planview ProjectPlace API into Microsoft Fabric:

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

@dlt.source
def planview_projectplace_source(client_id=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.projectplace.com/1",
            "auth": {"type": "bearer", "token": client_id},
        },
        "resources": [
            {"name": "workspaces", "endpoint": {"path": "2/account/projects"}},
            {"name": "webhooks", "endpoint": {"path": "1/webhooks/list"}}
        ],
    }
    yield from rest_api_resources(config)


def load_planview_projectplace_to_fabric() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="planview_projectplace_pipeline",
        destination="fabric",
        dataset_name="planview_projectplace_data",
    )
    load_info = pipeline.run(planview_projectplace_source())
    print(load_info)


if __name__ == "__main__":
    load_planview_projectplace_to_fabric()

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

---

How do I query Planview ProjectPlace 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):

Copy
import dlt

data = dlt.pipeline("planview_projectplace_pipeline").dataset()
df = data.webhooks.df()
print(df.head())

SQL:

Copy
SELECT * FROM planview_projectplace_data.webhooks LIMIT 10;

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

---

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

Book a demo →

---

What other destinations can I load Planview ProjectPlace 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.

---

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