Load Rocketchat data to DuckDB

Rocket.Chat is a communication platform that provides a REST API to manage resources such as users, channels, and messages. Everything needed to build a working Rocketchat → 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 Rocketchat 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 Rocketchat 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 Rocketchat 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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Rocketchat API at a glance

These values come from the Rocketchat API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Rocketchat API?

All authenticated requests must include the 'X-Auth-Token' and 'X-User-Id' headers, which are returned after a successful login.

1. Get your credentials

To obtain credentials for the Rocket.Chat REST API: 1. Log in to your Rocket.Chat workspace. 2. Click on your profile picture in the top-left corner and select 'My Account'. 3. Navigate to the 'Personal Access Tokens' section in the sidebar. 4. Enter a name for your token in the 'Add Personal Access Token' field and click 'Add'. 5. Copy the generated token immediately, as it will not be displayed again. You will also need your User ID, which is available in your 'Account' settings under 'Profile' or by inspecting your user object via the API.

2. Add them to .dlt/secrets.toml

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[sources.rocketchat_source]
rocket_chat_user_id = "your_user_id_here"
rocket_chat_auth_token = "your_personal_access_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.

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What Rocketchat data can I load into DuckDB?

These are the Rocketchat endpoints dlt can load into DuckDB:

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How do I load only new Rocketchat records?

Rocketchat exposes _updatedAt on api/v1/rooms.get, 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": "rooms_sync", "endpoint": {
    "path": "api/v1/rooms.get",
    "data_selector": "update",
    "incremental": {"cursor_path": "_updatedAt", "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 Rocketchat pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading login and users.getPersonalAccessTokens from the Rocketchat API into DuckDB:

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

@dlt.source
def rocketchat_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "The base URL is the domain or site URL of your Rocket.Chat workspace (e.g., https://your-workspace.rocket.chat/api/v1)",
            "auth": {"type": "api_key", "api_key": api_key, "name": "X-Auth-Token", "location": "header"},
        },
        "resources": [
            {"name": "rooms_sync", "endpoint": {"path": "api/v1/rooms.get", "data_selector": "update"}},
            {"name": "subscriptions_sync", "endpoint": {"path": "api/v1/subscriptions.get", "data_selector": "update"}}
        ],
    }
    yield from rest_api_resources(config)


def load_rocketchat_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="rocketchat_pipeline",
        destination="duckdb",
        dataset_name="rocketchat_data",
    )
    load_info = pipeline.run(rocketchat_source())
    print(load_info)


if __name__ == "__main__":
    load_rocketchat_to_duckdb()

Run it with python rocketchat_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 Rocketchat 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("rocketchat_pipeline").dataset()
df = data.chat_sync.df()
print(df.head())

SQL:

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SELECT * FROM rocketchat_data.chat_sync LIMIT 10;

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

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How do I deploy the Rocketchat 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 Rocketchat 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 Rocketchat 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