Load Sumo Logic data to DuckDB

Sumo Logic provides a suite of APIs for managing and interacting with log data, metrics, and security configurations in the Sumo Logic platform. Everything needed to build a working Sumo Logic → 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 Sumo Logic 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 Sumo Logic 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 Sumo Logic 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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Sumo Logic API at a glance

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

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How do I authenticate with the Sumo Logic API?

Sumo Logic APIs support Basic Authentication using an Access ID and Access Key, which should be provided in the Authorization header as a base64-encoded string ('Basic <base64(accessId:accessKey)>'). Alternatively, they support OAuth authentication by providing an access token in the Authorization header ('Bearer ').

1. Get your credentials

1. Log in to the Sumo Logic web interface. 2. Navigate to your username in the top menu and select 'Preferences' (or 'Personal Access Keys'). Alternatively, administrators can navigate to 'Administration' > 'Account Security Settings' > 'Access Keys' (or 'Service Accounts' for non-user-specific keys). 3. Click '+ Add Access Key' in the upper right. 4. Enter a name for the key and configure necessary permissions/scopes. 5. Click 'Save'. 6. Copy the 'Access ID' and 'Access Key' displayed in the popup window. Ensure you save them securely, as they will not be visible again after closing the popup.

2. Add them to .dlt/secrets.toml

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[sources.sumo_logic_source]
access_id, access_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 Sumo Logic data can I load into DuckDB?

These are the Sumo Logic endpoints dlt can load into DuckDB:

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How do I load only new Sumo Logic records?

The Sumo Logic 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.

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{"name": "collectors", "endpoint": {
    "path": "api/v1/collectors",
    # 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.

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What does the generated Sumo Logic pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading search and collectors from the Sumo Logic API into DuckDB:

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

@dlt.source
def sumo_logic_source(access_id_access_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "The base URL depends on the user's Sumo Logic deployment, typically following the format 'https://api.<deployment>.sumologic.com/api/' (e.g., 'https://api.sumologic.com/api/' for US1).",
            "auth": {"type": "http_basic", "username": "REPLACE_ME", "password": access_id_access_key},
        },
        "resources": [
            {"name": "collectors", "endpoint": {"path": "api/v1/collectors", "data_selector": "collectors"}},
            {"name": "users", "endpoint": {"path": "api/v1/users", "data_selector": "users"}}
        ],
    }
    yield from rest_api_resources(config)


def load_sumo_logic_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="sumo_logic_pipeline",
        destination="duckdb",
        dataset_name="sumo_logic_data",
    )
    load_info = pipeline.run(sumo_logic_source())
    print(load_info)


if __name__ == "__main__":
    load_sumo_logic_to_duckdb()

Run it with python sumo_logic_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 Sumo Logic 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("sumo_logic_pipeline").dataset()
df = data.collectors.df()
print(df.head())

SQL:

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SELECT * FROM sumo_logic_data.collectors LIMIT 10;

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

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How do I deploy the Sumo Logic 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 Sumo Logic 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 Sumo Logic 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