Liftoff Python API Docs | dltHub

Liftoff is a mobile advertising platform that provides an Advertiser Reporting API to programmatically generate and download campaign reports and fetch account entities (apps, campaigns, creatives, events, customer). The REST API base URL is https://data.liftoff.io/api/v1 and All requests use HTTP Basic auth with API_KEY as username and API_SECRET as password..

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 Liftoff data in under 10 minutes.

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What data can I load from Liftoff?

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

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How do I authenticate with the Liftoff API?

Use HTTP Basic auth on every request: set the Authorization header via standard basic auth (username=API_KEY, password=API_SECRET). Example curl uses --user 'API_KEY:API_SECRET'.

1. Get your credentials

1. Contact your Liftoff account manager to request API credentials for the Reporting API.
2. Liftoff will provide an API key and API secret (emailed).
3. Use the API key as the HTTP Basic username and the API secret as the password when making requests.

2. Add them to .dlt/secrets.toml

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[sources.liftoff_source]
api_key = "your_api_key_here"
api_secret = "your_api_secret_here"

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.

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How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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

1. Install the dlt AI Workbench:

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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:

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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 Liftoff 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:

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python liftoff_pipeline.py

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

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

Inspect your pipeline and data:

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dlt pipeline liftoff_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.

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Python pipeline example

This example loads reports and apps from the Liftoff API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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

@dlt.source
def liftoff_source(api_key, api_secret=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://data.liftoff.io/api/v1",
            "auth": {
                "type": "http_basic",
                "username": api_key, api_secret,
            },
        },
        "resources": [
            {"name": "reports", "endpoint": {"path": "reports"}},
            {"name": "apps", "endpoint": {"path": "apps"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="liftoff_pipeline",
        destination="duckdb",
        dataset_name="liftoff_data",
    )
    load_info = pipeline.run(liftoff_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.

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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):

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import dlt

data = dlt.pipeline("liftoff_pipeline").dataset()
sessions_df = data.reports.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM liftoff_data.reports LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("liftoff_pipeline").dataset()
data.reports.df().head()

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

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What destinations can I load Liftoff 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.

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Troubleshooting

Authentication failures

If you receive 401/403, verify you are using HTTP Basic auth with API_KEY as username and API_SECRET as password. Ensure credentials are not URL‑encoded in the --user parameter and that whitespace/newlines are not included.

Rate limits and headers

Endpoints are rate‑limited; responses include x-rate-limit-max and x-rate-limit-remaining headers. Common limits: /reports GET 60/hr, /reports POST 30/hr (test=true 60/hr), /reports/{id}/status 1000/hr, /reports/{id}/data 30/hr, entity endpoints ~60/hr. When receiving 429, back off and retry per header guidance.

Report pagination & data format

Report metadata and entity endpoints return top‑level JSON arrays. Report data (/reports/{id}/data) returns an object with "columns" (column names) and "rows" (array of row arrays) — use rows as the record selector and map each row to the columns header array.

Common error response format

Errors return JSON with fields: error_type (string), message (string), errors (nullable array). Example: { "error_type":"BAD REQUEST","message":"Please correct the following issues with the request.","errors":["start_time should be before end_time."]}

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

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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.

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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