Load Fireblocks data to DuckDB

Fireblocks provides a robust REST API for managing digital assets, accounts, and transactions programmatically. Everything needed to build a working Fireblocks → 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 Fireblocks 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 Fireblocks 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 Fireblocks 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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Fireblocks API at a glance

These values come from the Fireblocks API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Fireblocks API?

All requests must be authenticated using an API key and a request-signing process. The request requires two headers: 'X-API-Key' (your API User ID) and 'Authorization' set to 'Bearer ', where the JWT is a Base64-encoded token signed with your RSA private key.

1. Get your credentials

1. Generate an RSA 4096 private key and a Certificate Signing Request (CSR) file using OpenSSL: 'openssl req -new -newkey rsa:4096 -nodes -keyout fireblocks_secret.key -out fireblocks.csr -subj '/O=<your_organization>''. 2. Log in to the Fireblocks Console and navigate to the Developer Center > API Users page. 3. Click 'Add API user', provide a display name, and select the appropriate role. 4. Upload the 'fireblocks.csr' file generated in step 1. 5. Once the request is approved (if applicable), the API User (ID) will appear in the API users list. Copy this ID; it serves as your API key. Keep the 'fireblocks_secret.key' file secure, as it is required to sign all API requests.

2. Add them to .dlt/secrets.toml

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[sources.fireblocks_source]
fireblocks_api_key = "your_api_user_id_here"
fireblocks_secret_key_path = "/path/to/your/fireblocks_secret.key"

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 Fireblocks data can I load into DuckDB?

These are the Fireblocks endpoints dlt can load into DuckDB:

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How do I load only new Fireblocks records?

Fireblocks exposes after on v1/vault/accounts_paged, 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": "vault_accounts", "endpoint": {
    "path": "v1/vault/accounts_paged",
    "data_selector": "accounts",
    "incremental": {"cursor_path": "after", "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 Fireblocks pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading vault/accounts and transactions from the Fireblocks API into DuckDB:

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

@dlt.source
def fireblocks_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.fireblocks.io/v1",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "vault_accounts", "endpoint": {"path": "v1/vault/accounts_paged", "data_selector": "accounts"}},
            {"name": "assets", "endpoint": {"path": "v1/supported_assets"}}
        ],
    }
    yield from rest_api_resources(config)


def load_fireblocks_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="fireblocks_pipeline",
        destination="duckdb",
        dataset_name="fireblocks_data",
    )
    load_info = pipeline.run(fireblocks_source())
    print(load_info)


if __name__ == "__main__":
    load_fireblocks_to_duckdb()

Run it with python fireblocks_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 Fireblocks 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("fireblocks_pipeline").dataset()
df = data.vault_accounts.df()
print(df.head())

SQL:

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SELECT * FROM fireblocks_data.vault_accounts LIMIT 10;

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

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How do I deploy the Fireblocks 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 Fireblocks 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 Fireblocks 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