Load SYSTRAN Translate API data to DuckDB

SYSTRAN Translate API provides neural machine translation, language detection, and NLP services for text, HTML, and file translation. Everything needed to build a working SYSTRAN Translate API → 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 SYSTRAN Translate API to DuckDB pipeline

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

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Run uvx dlthub-init@latest to build a pipeline from SYSTRAN Translate API 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 SYSTRAN Translate API 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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SYSTRAN Translate API API at a glance

These values come from the SYSTRAN Translate API API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the SYSTRAN Translate API API?

Authentication requires either an API Key or an OAuth2 access token. The API Key can be passed in the 'Authorization' header as 'Key YOUR_API_KEY' or as a 'key' query parameter; access tokens are passed in the 'Authorization' header as 'Bearer YOUR_ACCESS_TOKEN'.

1. Get your credentials

To obtain an API key for the SYSTRAN Translate API, log in to your account on the SYSTRAN Translate portal (https://translate.systran.net). Click on the User menu located in the upper right corner of the screen and select 'Your Personal Info'. Navigate to the 'API Keys' section (sometimes labeled 'TRS Keys' depending on your specific plan) and click the button to generate or create a new API key. Ensure you have an active API, PREMIUM, or INFINITE subscription, as API access is restricted to these plans.

2. Add them to .dlt/secrets.toml

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[sources.systran_translate_api_source]
systran_api_key = "your_api_key_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.

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What SYSTRAN Translate API data can I load into DuckDB?

These are the SYSTRAN Translate API endpoints dlt can load into DuckDB:

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How do I load only new SYSTRAN Translate API records?

SYSTRAN Translate API exposes skip on resources/dictionary/list, 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": "dictionary", "endpoint": {
    "path": "resources/dictionary/list",
    "incremental": {"cursor_path": "skip", "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 SYSTRAN Translate API pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /translation/text/translate and /translation/apiVersion from the SYSTRAN Translate API API into DuckDB:

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

@dlt.source
def systran_translate_api_source(key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api-translate.systran.net",
            "auth": {"type": "bearer", "token": key},
        },
        "resources": [
            {"name": "dictionary", "endpoint": {"path": "resources/dictionary/list"}},
            {"name": "corpus", "endpoint": {"path": "resources/corpus/list"}}
        ],
    }
    yield from rest_api_resources(config)


def load_systran_translate_api_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="systran_translate_api_pipeline",
        destination="duckdb",
        dataset_name="systran_translate_api_data",
    )
    load_info = pipeline.run(systran_translate_api_source())
    print(load_info)


if __name__ == "__main__":
    load_systran_translate_api_to_duckdb()

Run it with python systran_translate_api_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 SYSTRAN Translate API 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("systran_translate_api_pipeline").dataset()
df = data.dictionary.df()
print(df.head())

SQL:

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SELECT * FROM systran_translate_api_data.dictionary LIMIT 10;

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

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How do I deploy the SYSTRAN Translate API 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 SYSTRAN Translate API 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 SYSTRAN Translate API 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