Load Coinbase Prime data to DuckDB

Coinbase Prime is an institutional-grade platform for managing cryptocurrency trading, custody, and portfolio operations. Everything needed to build a working Coinbase Prime → 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 Coinbase Prime 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 Coinbase Prime 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 Coinbase Prime 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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Coinbase Prime API at a glance

These values come from the Coinbase Prime API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Coinbase Prime API?

All requests require HMAC SHA-256 signature authentication via custom HTTP headers: X-CB-ACCESS-KEY, X-CB-ACCESS-PASSPHRASE, X-CB-ACCESS-SIGNATURE, and X-CB-ACCESS-TIMESTAMP. The signature is generated by HMAC SHA-256 hashing a concatenated string of the timestamp, method, path, and request body with the secret key, then base64-encoding the result.

1. Get your credentials

1. Sign in to your Coinbase Prime account. 2. Navigate to the lower-left corner and click the Gear (Settings) icon. 3. Select 'APIs'. 4. Click 'Create API Key'. 5. Enter the API name, access type, and expiration date, then click Continue. 6. Follow the on-screen prompts to verify your identity (usually via YubiKey) and perform any required consensus approvals. 7. Once the status shows as 'Pending', find your key under 'Pending Keys' and click 'Activate Key' to finalize. Note: Keep your API key, secret key, and passphrase secure as they will be required for authentication.

2. Add them to .dlt/secrets.toml

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[sources.coinbase_prime_source]
access_key = "your_access_key"
passphrase = "your_passphrase"
signing_key = "your_signing_key"
portfolio_id = "your_portfolio_id"

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 Coinbase Prime data can I load into DuckDB?

These are the Coinbase Prime endpoints dlt can load into DuckDB:

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How do I load only new Coinbase Prime records?

Coinbase Prime exposes next_cursor on v1/portfolios, 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": "portfolios", "endpoint": {
    "path": "v1/portfolios",
    "incremental": {"cursor_path": "next_cursor", "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 Coinbase Prime pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /v1/portfolios and /v1/accounts from the Coinbase Prime API into DuckDB:

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

@dlt.source
def coinbase_prime_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.prime.coinbase.com/v1",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "portfolios", "endpoint": {"path": "v1/portfolios"}},
            {"name": "order_fills", "endpoint": {"path": "v1/portfolios/{portfolio_id}/orders/{order_id}/fills"}}
        ],
    }
    yield from rest_api_resources(config)


def load_coinbase_prime_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="coinbase_prime_pipeline",
        destination="duckdb",
        dataset_name="coinbase_prime_data",
    )
    load_info = pipeline.run(coinbase_prime_source())
    print(load_info)


if __name__ == "__main__":
    load_coinbase_prime_to_duckdb()

Run it with python coinbase_prime_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 Coinbase Prime 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("coinbase_prime_pipeline").dataset()
df = data.portfolios.df()
print(df.head())

SQL:

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SELECT * FROM coinbase_prime_data.portfolios LIMIT 10;

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

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How do I deploy the Coinbase Prime 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 Coinbase Prime 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 Coinbase Prime 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