Load Revvity Signals data to DuckDB

Revvity Signals REST API provides programmatic access to Signals Research Suite data, including experiments, entities, and workflow operations. Everything needed to build a working Revvity Signals → 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 Revvity Signals 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 Revvity Signals 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 Revvity Signals 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.

---

Revvity Signals API at a glance

These values come from the Revvity Signals API reference — the authoritative source if anything here looks out of date.

---

How do I authenticate with the Revvity Signals API?

Authentication is required via the Authorization header using the 'Bearer' scheme, followed by the API key (e.g., 'Authorization: Bearer <API_KEY>'). Alternatively, an 'x-api-key' header can be used with the API key as the value.

1. Get your credentials

To obtain an API key for the Revvity Signals REST API, log in to your Signals instance as an administrator. Navigate to the Configuration menu, select System Settings, and then click on the API Key section. From there, select Add New API Key (or provide your email address if prompted), choose the associated user, and click Generate API Key. Note that keys must be associated with an existing user account in the tenant.

2. Add them to .dlt/secrets.toml

Copy
[sources.revvity_signals_source]
api_key = "your_generated_api_key_here"
# Include the following in your request headers:
# Authorization = "Bearer your_generated_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.

---

What Revvity Signals data can I load into DuckDB?

These are the Revvity Signals endpoints dlt can load into DuckDB:

---

How do I load only new Revvity Signals records?

Revvity Signals exposes modifiedAt on entities/search, 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": "entities_search", "endpoint": {
    "path": "entities/search",
    "data_selector": "data",
    "incremental": {"cursor_path": "modifiedAt", "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 Revvity Signals pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /users and /entities/search/terms from the Revvity Signals API into DuckDB:

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

@dlt.source
def revvity_signals_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://<tenant>.signalsresearch.revvitycloud.com/api/rest/v1.0/",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "entities_search", "endpoint": {"path": "entities/search", "data_selector": "data"}},
            {"name": "users", "endpoint": {"path": "users", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def load_revvity_signals_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="revvity_signals_pipeline",
        destination="duckdb",
        dataset_name="revvity_signals_data",
    )
    load_info = pipeline.run(revvity_signals_source())
    print(load_info)


if __name__ == "__main__":
    load_revvity_signals_to_duckdb()

Run it with python revvity_signals_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 Revvity Signals 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("revvity_signals_pipeline").dataset()
df = data.entities_search.df()
print(df.head())

SQL:

Copy
SELECT * FROM revvity_signals_data.entities_search LIMIT 10;

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

---

How do I deploy the Revvity Signals 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 Revvity Signals 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 Revvity Signals 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