Load ThoughtSpot data to DuckDB

ThoughtSpot is an analytics platform that offers a REST API for managing users, sessions, and data objects. Everything needed to build a working ThoughtSpot → 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.

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Build your ThoughtSpot 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 ThoughtSpot 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 ThoughtSpot 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.

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ThoughtSpot API at a glance

These values come from the ThoughtSpot API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the ThoughtSpot API?

Requests require an Authorization header with a Bearer token, e.g., 'Authorization: Bearer {token}'. The token is typically obtained via a POST request to /api/rest/2.0/auth/token/full or /api/rest/2.0/auth/token/object.

1. Get your credentials

For programmatic API authentication, ThoughtSpot provides two primary methods: Trusted Authentication or API Tokens. \n\n1. Trusted Authentication: \n- Log in to your ThoughtSpot instance as an administrator. \n- Navigate to Admin settings and enable 'Trusted authentication'. \n- Once enabled, the system generates a 'secret_key'. Copy this key securely, as it will be required to request authentication tokens. \n\n2. API Tokens (Analyst Studio): \n- Navigate to Workspace Settings > Features > API Keys. \n- For Workspace-level access (recommended for pipelines), select 'Workspace keys' and click 'Create API Key'. Save the provided 'Token' and 'Secret' immediately, as the Secret is only displayed once. \n- For individual user access, ensure 'Member keys' are enabled by an admin, then navigate to Workspace Settings > Personal > My API Keys to generate a personal token.

2. Add them to .dlt/secrets.toml

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[sources.thoughtspot_source]
api_key = "REPLACE_ME"

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.

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What ThoughtSpot data can I load into DuckDB?

These are the ThoughtSpot endpoints dlt can load into DuckDB:

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How do I load only new ThoughtSpot records?

ThoughtSpot exposes record_offset on api/rest/2.0/metadata/search, so dlt can request only the records that changed since the last run. Set it as the cursor_path and dlt tracks the high-water mark for you between runs.

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{"name": "metadata_search", "endpoint": {
    "path": "api/rest/2.0/metadata/search",
    "incremental": {"cursor_path": "record_offset", "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.

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What does the generated ThoughtSpot pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /api/rest/2.0/auth/token/full and /api/rest/2.0/auth/session/login from the ThoughtSpot API into DuckDB:

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

@dlt.source
def thoughtspot_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://<THOUGHTSPOT_HOST>/api/rest/2.0",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "metadata_search", "endpoint": {"path": "api/rest/2.0/metadata/search"}},
            {"name": "connection_search", "endpoint": {"path": "api/rest/2.0/connection/search"}}
        ],
    }
    yield from rest_api_resources(config)


def load_thoughtspot_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="thoughtspot_pipeline",
        destination="duckdb",
        dataset_name="thoughtspot_data",
    )
    load_info = pipeline.run(thoughtspot_source())
    print(load_info)


if __name__ == "__main__":
    load_thoughtspot_to_duckdb()

Run it with python thoughtspot_pipeline.py. The agent iterates on this until it loads cleanly — you review and approve, rather than write it from scratch.

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How do I query ThoughtSpot 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):

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import dlt

data = dlt.pipeline("thoughtspot_pipeline").dataset()
df = data.metadata_search.df()
print(df.head())

SQL:

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SELECT * FROM thoughtspot_data.metadata_search LIMIT 10;

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

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How do I deploy the ThoughtSpot 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 ThoughtSpot loads into governed, documented models.
• Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →

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What other destinations can I load ThoughtSpot 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.

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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