Anaplan Python API Docs | dltHub

Anaplan is a cloud planning platform that exposes a RESTful Integration API (v2.0) for managing bulk and transactional data operations against Anaplan workspaces and models. The REST API base URL is https://api.anaplan.com/2/0/ and Authentication requires obtaining a token via the Anaplan auth endpoint (username/password or client certificate) and sending it in request headers..

dlt is an open-source Python library that handles authentication, pagination, and schema evolution automatically. dlthub provides AI context files that enable code assistants to generate production-ready pipelines. Install with uv pip install "dlt[workspace]" and start loading Anaplan data in under 10 minutes.

---

What data can I load from Anaplan?

Here are some of the endpoints you can load from Anaplan:

---

How do I authenticate with the Anaplan API?

Anaplan uses token‑based authentication; obtain a bearer token from the auth endpoint and include it in the Authorization header of all API calls.

1. Get your credentials

1. Sign into the Anaplan Admin console (or ask your tenant admin).
2. Create a service user or use an existing user with access to the needed workspaces/models.
3. For password‑based access, note the username and password.
4. For certificate‑based access, generate a client certificate and register it in Anaplan.
5. Call the Anaplan auth endpoint to exchange the credentials or certificate for an authentication token.

2. Add them to .dlt/secrets.toml

Copy
[sources.anaplan_source]
anaplan_auth = { username = "your_username", password = "your_password" }

dlt reads this automatically at runtime — never hardcode tokens in your pipeline script. For production environments, see setting up credentials with dlt for environment variable and vault-based options.

---

How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

Copy
uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

Copy
dlt ai init --agent <your-agent> # <agent>: claude | cursor | codex

This installs project rules, a secrets management skill, appropriate ignore files, and configures the dlt MCP server for your agent. Learn more →

2. Install the rest-api-pipeline toolkit:

Copy
dlt ai toolkit rest-api-pipeline install

This loads the skills and context about dlt the agent uses to build the pipeline iteratively, efficiently, and safely. The agent uses MCP tools to inspect credentials — it never needs to read your secrets.toml directly. Learn more →

3. Start LLM-assisted coding:

Use /find-source to load data from the Anaplan API into DuckDB.

The rest-api-pipeline toolkit takes over from here — it reads relevant API documentation, presents you with options for which endpoints to load, and follows a structured workflow to scaffold, debug, and validate the pipeline step by step.

4. Run the pipeline:

Copy
python anaplan_pipeline.py

If everything is configured correctly, you'll see output like this:

Copy
Pipeline anaplan_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset anaplan_data
The duckdb destination used duckdb:/anaplan.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

Copy
dlt pipeline anaplan_pipeline show

This opens the Pipeline Dashboard where you can verify pipeline state, load metrics, schema (tables, columns, types), and query the loaded data directly.

---

Python pipeline example

This example loads workspaces and models from the Anaplan API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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

@dlt.source
def anaplan_source(anaplan_auth=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.anaplan.com/2/0/",
            "auth": {
                "type": "http_basic",
                "token": anaplan_auth,
            },
        },
        "resources": [
            {"name": "workspaces", "endpoint": {"path": "workspaces", "data_selector": "workspaces"}},
            {"name": "models", "endpoint": {"path": "workspaces/{workspaceId}/models", "data_selector": "models"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="anaplan_pipeline",
        destination="duckdb",
        dataset_name="anaplan_data",
    )
    load_info = pipeline.run(anaplan_source())
    print(load_info)

To add more endpoints, append entries from the resource table to the "resources" list using the same name, path, and data_selector pattern.

---

How do I query the loaded data?

Once the pipeline runs, dlt creates one table per resource. You can query with Python or SQL.

Python (pandas DataFrame):

Copy
import dlt

data = dlt.pipeline("anaplan_pipeline").dataset()
sessions_df = data.workspaces.df()
print(sessions_df.head())

SQL (DuckDB example):

Copy
SELECT * FROM anaplan_data.workspaces LIMIT 10;

In a marimo or Jupyter notebook:

Copy
import dlt

data = dlt.pipeline("anaplan_pipeline").dataset()
data.workspaces.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

---

What destinations can I load Anaplan data to?

dlt supports loading into any of these destinations — only the destination parameter changes:

Change the destination in dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. See the full destinations list.

---

Troubleshooting

Authentication failures

If you receive 401/403 responses, verify you exchanged credentials or client certificate at the Anaplan auth endpoint and that the returned token is sent in the required header. Ensure the user has access to targeted workspaces/models.

Rate limits and allowlist

Anaplan enforces URL/IP allowlist and may throttle heavy usage; follow Anaplan URL, IP and allowlist guidance and batch large bulk operations via bulk APIs.

Pagination and large bulk responses

Use the bulk APIs for large datasets. Many list endpoints return objects with arrays under keys such as "workspaces", "models", "exports", etc.; confirm the data selector for each endpoint by inspecting the response body.

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

---

Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI Workbench:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-runtime — Deploy, schedule, and monitor your pipeline in production.

Copy
dlt ai toolkit data-exploration install
dlt ai toolkit dlthub-runtime install

• How to deploy a pipeline
• How to explore your data in marimo Notebooks
• How to query your data in Python with dataset
• Join the dlt community on Slack