Load Talkdesk data to DuckDB

Talkdesk provides a suite of RESTful APIs for managing account data, telephony resources, and partner integrations across multiple regions. Everything needed to build a working Talkdesk → 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 Talkdesk 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 Talkdesk 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 Talkdesk 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.

---

Talkdesk API at a glance

These values come from the Talkdesk API reference — the authoritative source if anything here looks out of date.

---

How do I authenticate with the Talkdesk API?

Authentication is handled via OAuth 2.0; requests must include an 'Authorization' header with a Bearer token.

1. Get your credentials

To obtain API credentials for Talkdesk, you must use the Talkdesk Builder interface. Navigate to the 'OAuth Clients' section within the Builder tool, select 'New OAuth Client,' and provide the required name and grant type (e.g., 'Client credentials'). Ensure you select the necessary API scopes for your integration. Once configured, click 'Download' to save the OAuth JSON file, which contains your 'Client ID' and 'Client secret' required for authentication. If the OAuth Clients tab is unavailable, contact Talkdesk support or your account representative to request API access permissions.

2. Add them to .dlt/secrets.toml

Copy
[sources.talkdesk_source]
talkdesk_client_id = "your_client_id_here"
talkdesk_client_secret = "your_client_secret_here"
talkdesk_base_url = "https://api.talkdeskapp.com/"

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 Talkdesk data can I load into DuckDB?

These are the Talkdesk endpoints dlt can load into DuckDB:

---

How do I load only new Talkdesk records?

Talkdesk exposes updated_at on record-lists, 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.

Copy
{"name": "record_lists", "endpoint": {
    "path": "record-lists",
    "data_selector": "record_lists",
    "incremental": {"cursor_path": "updated_at", "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 Talkdesk pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /oauth/token and /reports/calls/jobs from the Talkdesk API into DuckDB:

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

@dlt.source
def talkdesk_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.talkdeskapp.com/",
            "auth": {"type": "bearer", "token": access_token},
        },
        "resources": [
            {"name": "record_lists", "endpoint": {"path": "record-lists", "data_selector": "record_lists"}},
            {"name": "contacts", "endpoint": {"path": "contacts", "data_selector": "contacts"}}
        ],
    }
    yield from rest_api_resources(config)


def load_talkdesk_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="talkdesk_pipeline",
        destination="duckdb",
        dataset_name="talkdesk_data",
    )
    load_info = pipeline.run(talkdesk_source())
    print(load_info)


if __name__ == "__main__":
    load_talkdesk_to_duckdb()

Run it with python talkdesk_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 Talkdesk 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("talkdesk_pipeline").dataset()
df = data.record_lists.df()
print(df.head())

SQL:

Copy
SELECT * FROM talkdesk_data.record_lists LIMIT 10;

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

---

How do I deploy the Talkdesk 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 Talkdesk 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 Talkdesk 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