Bigin by Zoho Python API Docs | dltHub

Bigin is a lightweight CRM by Zoho for small businesses that provides REST APIs to manage modules such as Contacts, Deals, Accounts (Companies), Activities (Tasks, Calls, Events), Products, Notes, and related metadata. The REST API base URL is https://www.zohoapis.com/bigin/v1 and All requests require an OAuth2 access token (Bearer-style) 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 Bigin by Zoho data in under 10 minutes.

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What data can I load from Bigin by Zoho?

Here are some of the endpoints you can load from Bigin by Zoho:

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How do I authenticate with the Bigin by Zoho API?

Authentication uses OAuth2 access tokens, which are sent in the Authorization header as Zoho-oauthtoken {access_token}. Scopes use ZohoBigin.* naming conventions.

1. Get your credentials

To obtain API credentials, create client credentials in the Zoho API Console. Then, use standard OAuth flows (such as the authorization code flow) to get a refresh token and an access token. Access tokens expire, so use the refresh token to obtain new access tokens as needed.

2. Add them to .dlt/secrets.toml

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[sources.bigin_zoho_source]
access_token = "your_access_token_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 Bigin by Zoho 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 bigin_zoho_pipeline.py

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

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Pipeline bigin_zoho_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset bigin_zoho_data
The duckdb destination used duckdb:/bigin_zoho.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 bigin_zoho_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 contacts and accounts from the Bigin by Zoho 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 bigin_zoho_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://www.zohoapis.com/bigin/v1",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            {"name": "contacts", "endpoint": {"path": "contacts", "data_selector": "data"}},
            {"name": "accounts", "endpoint": {"path": "accounts", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="bigin_zoho_pipeline",
        destination="duckdb",
        dataset_name="bigin_zoho_data",
    )
    load_info = pipeline.run(bigin_zoho_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("bigin_zoho_pipeline").dataset()
sessions_df = data.contacts.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM bigin_zoho_data.contacts LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("bigin_zoho_pipeline").dataset()
data.contacts.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 Bigin by Zoho 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

Common API Errors

The Bigin by Zoho API can return several error codes indicating issues with requests:

• INVALID_URL_PATTERN (HTTP 404): Occurs when the requested URL is incorrect.
• OAUTH_SCOPE_MISMATCH (HTTP 401): Indicates that the OAuth scope used is insufficient for the requested operation.
• NO_PERMISSION (HTTP 403): Signifies that the authenticated user lacks the necessary permissions.
• INTERNAL_ERROR (HTTP 500): A generic server-side error.
• INVALID_REQUEST_METHOD (HTTP 400): Returned when an incorrect HTTP method is used for an endpoint.
• AUTHORIZATION_FAILED (HTTP 400): Indicates insufficient privileges for the action.

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