Load TTAPI data to DuckDB

TTAPI provides a suite of AI-powered RESTful services for music, image, and language processing tasks. Everything needed to build a working TTAPI → 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 TTAPI 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 TTAPI 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 TTAPI 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.

---

TTAPI API at a glance

These values come from the TTAPI API documentation. Check them against the vendor's current reference before relying on them in production.

---

How do I authenticate with the TTAPI API?

All requests must include the API key in the request header named 'TT-API-KEY'.

1. Get your credentials

To obtain credentials for the Trading Technologies (TT) REST API, follow these steps in the TT platform: 1. Ensure your company administrator has enabled the 'Can create TT Rest API key' permission for your user account. 2. Log into the TT Platform Setup application (use the UAT environment for development or Live for production). 3. Navigate to the 'Users' section, select your user, and go to the 'App Keys' tab. 4. Click 'New' to create a new application key. 5. Select 'TT REST API' as the Application Key Type, choose a usage plan, and click 'Create'. 6. Copy the generated application key value (displayed in the 'Value' column) and the application secret (displayed in the 'Secret' field or via 'Copy Secret to Clipboard'). You will need both to generate an authentication token.

2. Add them to .dlt/secrets.toml

Copy
[sources.ttapi_source]
tt_api_key = "your_application_key_here"
tt_api_secret = "your_application_secret_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 TTAPI data can I load into DuckDB?

These are the TTAPI endpoints dlt can load into DuckDB:

---

How do I load only new TTAPI records?

The TTAPI 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": "midjourney_imagine", "endpoint": {
    "path": "midjourney/v1/imagine",
    # 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 TTAPI pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /token and / (base API endpoints) from the TTAPI API into DuckDB:

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

@dlt.source
def ttapi_source(tt_api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.ttapi.io",
            "auth": {"type": "api_key", "api_key": tt_api_key, "name": "TT-API-KEY", "location": "header"},
        },
        "resources": [
            {"name": "midjourney_imagine", "endpoint": {"path": "midjourney/v1/imagine", "data_selector": "data"}},
            {"name": "midjourney_fetch", "endpoint": {"path": "midjourney/v1/fetch", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def load_ttapi_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="ttapi_pipeline",
        destination="duckdb",
        dataset_name="ttapi_data",
    )
    load_info = pipeline.run(ttapi_source())
    print(load_info)


if __name__ == "__main__":
    load_ttapi_to_duckdb()

Run it with python ttapi_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 TTAPI 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("ttapi_pipeline").dataset()
df = data.midjourney_fetch.df()
print(df.head())

SQL:

Copy
SELECT * FROM ttapi_data.midjourney_fetch LIMIT 10;

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

---

How do I deploy the TTAPI 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 TTAPI 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 TTAPI 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