Load Zoho WorkDrive data to DuckDB

Zoho WorkDrive is a cloud-based file storage and collaboration platform offering a REST API for managing files, folders, and team data. Everything needed to build a working Zoho WorkDrive → DuckDB 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 Zoho WorkDrive to DuckDB pipeline

Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.

PromptCopy prompt
Run uvx dlthub-init@latest to build a pipeline from Zoho WorkDrive to DuckDB and run it on dltHub

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 Zoho WorkDrive 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.

---

Zoho WorkDrive API at a glance

These values come from the Zoho WorkDrive API reference — the authoritative source if anything here looks out of date.

---

How do I authenticate with the Zoho WorkDrive API?

All requests require an OAuth2 access token passed in the Authorization header as 'Authorization: Zoho-oauthtoken <access_token>'.

1. Get your credentials

1. Navigate to the Zoho API Console (https://api-console.zoho.com). 2. Click Get Started and create a new project. 3. Select 'Server-based Applications' as the client type. 4. Provide your application details, including the Homepage URL and Authorized Redirect URIs. 5. Click Create to generate your Client ID and Client Secret. 6. Use these credentials to perform the OAuth2 Authorization Code flow to exchange your authorization code for an access token and an optional refresh token via the Zoho Accounts OAuth2 endpoint (https://accounts.zoho.com/oauth/v2/token).

2. Add them to .dlt/secrets.toml

Copy
[sources.zoho_workdrive_source]
client_id = "your_client_id_here"
client_secret = "your_client_secret_here"
refresh_token = "your_refresh_token_here"

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 Zoho WorkDrive data can I load into DuckDB?

These are the Zoho WorkDrive endpoints dlt can load into DuckDB:

---

How do I load only new Zoho WorkDrive records?

The Zoho WorkDrive API reference does not document a timestamp or sequence field for these endpoints, so there is nothing to advertise here as verified. Pick a field from the endpoints table above that increases with every write, then set it as the cursor_path.

Copy
{"name": "files", "endpoint": {
    "path": "files",
    # Replace with a field that increases on every write.
    "incremental": {"cursor_path": "REPLACE_ME", "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 Zoho WorkDrive pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /api/v1/files and /api/v1/folders from the Zoho WorkDrive API into DuckDB:

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

@dlt.source
def zoho_workdrive_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://www.zohoapis.com/workdrive/ (note: domain varies by region, e.g., .com, .eu, .in)",
            "auth": {"type": "http_basic", "username": "REPLACE_ME", "password": access_token},
        },
        "resources": [
            {"name": "files", "endpoint": {"path": "files", "data_selector": "data"}},
            {"name": "folders", "endpoint": {"path": "folders", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def load_zoho_workdrive_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="zoho_workdrive_pipeline",
        destination="duckdb",
        dataset_name="zoho_workdrive_data",
    )
    load_info = pipeline.run(zoho_workdrive_source())
    print(load_info)


if __name__ == "__main__":
    load_zoho_workdrive_to_duckdb()

Run it with python zoho_workdrive_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 Zoho WorkDrive data in DuckDB?

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("zoho_workdrive_pipeline").dataset()
df = data.files.df()
print(df.head())

SQL:

Copy
SELECT * FROM zoho_workdrive_data.files LIMIT 10;

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

---

How do I deploy the Zoho WorkDrive to DuckDB 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 Zoho WorkDrive 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 Zoho WorkDrive 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