Clevertap Python API Docs | dltHub

CleverTap is a customer engagement and analytics platform providing REST APIs to upload, retrieve, and manage user profiles, events, metrics and campaign data. The REST API base URL is https://api.clevertap.com/1 and All requests require header‑based account ID and passcode (project credentials) for authentication..

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 Clevertap data in under 10 minutes.

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What data can I load from Clevertap?

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

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How do I authenticate with the Clevertap API?

CleverTap uses header‑based authentication. Include X-CleverTap-Account-Id: ACCOUNT_ID and X-CleverTap-Passcode: PASSCODE on every request; Content-Type: application/json as appropriate.

1. Get your credentials

1. Log in to the CleverTap dashboard. 2) Open Project > View Project Credentials. 3) Copy the Project ID (Account ID). 4) Navigate to the Passcodes page and create or view a Passcode. 5) Use these values for the X-CleverTap-Account-Id and X-CleverTap-Passcode headers.

2. Add them to .dlt/secrets.toml

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[sources.clevertap_source]
account_id = "YOUR_ACCOUNT_ID"
passcode = "YOUR_PASSCODE"

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 Clevertap 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 clevertap_pipeline.py

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

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Pipeline clevertap_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset clevertap_data
The duckdb destination used duckdb:/clevertap.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 clevertap_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 profiles and upload from the Clevertap 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 clevertap_source(account_id, passcode=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.clevertap.com/1",
            "auth": {
                "type": "http_basic",
                "account_id, passcode": account_id, passcode,
            },
        },
        "resources": [
            {"name": "profiles", "endpoint": {"path": "profiles.json", "data_selector": "record"}},
            {"name": "upload", "endpoint": {"path": "upload", "data_selector": "d"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="clevertap_pipeline",
        destination="duckdb",
        dataset_name="clevertap_data",
    )
    load_info = pipeline.run(clevertap_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("clevertap_pipeline").dataset()
sessions_df = data.profiles.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM clevertap_data.profiles LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("clevertap_pipeline").dataset()
data.profiles.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 Clevertap 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 or 403, verify that the X-CleverTap-Account-Id and X-CleverTap-Passcode headers are correct and that the passcode is active for the project.

Rate limits and throttling

CleverTap may return a 429 status code when rate limits are exceeded. Implement exponential back‑off and retry logic.

Pagination and region endpoints

Use the region‑specific base URL (e.g., in1.api.clevertap.com, us1.api.clevertap.com) to access the correct data. Pagination parameters and response shapes can vary by endpoint; consult the endpoint‑specific documentation (e.g., profiles.json returns a "record" object for single‑profile responses).

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