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Load Revolut data to DuckDB

Build a Revolut to DuckDB pipeline with your coding agent. One prompt scaffolds it with the dltHub AI harness, plus the Revolut API base URL, auth, endpoints, and incremental loading.

SourceRevolutRevolut DocsDestinationDuckDBIn-process analytical database. The default local destination for dlt pipelines.

Revolut provides REST APIs for business account management, open banking integration, and merchant payment processing. Everything needed to build a working Revolut → 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 Revolut to DuckDB pipeline

Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.

Prompt
Run uvx dlthub-init@latest to build a pipeline from Revolut 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 Revolut 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.


Revolut API at a glance

Base URLhttps://b2b.revolut.com/api/1.0
Example endpointGET transactions
AuthenticationAll requests require an Authorization header with a Bearer token — sent in the Authorization header, prefixed Bearer
PaginationCursor-based via page_token, next cursor at next_page_token, page size via limit (default 100, max 500). When using page_token, you must include all query parameters from the initial request to maintain consistent filtering across pages. Note that some Revolut API endpoints (e.g., Revolut X) may use 'cursor' as the parameter and 'metadata.next_cursor' as the response path instead.
Incremental fieldcreated_at
Record idid
API referencehttps://developer.revolut.com/docs/api/business

These values come from the Revolut API reference — the authoritative source if anything here looks out of date.


How do I authenticate with the Revolut API?

Requests require an Authorization header formatted as 'Bearer '. Depending on the specific API, the token is either an access token obtained via OAuth or a Merchant secret API key.

1. Get your credentials

To obtain API credentials for the Revolut API, log in to your Revolut Business account. For Merchant API access, navigate to the Merchant overview, select 'Merchant API', and click 'Generate' to create your Production API Secret key. For Business API access, navigate to Settings > APIs > Business API in your Revolut Business web app to configure certificates, whitelist IP addresses, and obtain your initial access and refresh tokens.

2. Add them to .dlt/secrets.toml

[sources.revolut_source] api_key = "your_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 Revolut data can I load into DuckDB?

These are the Revolut endpoints dlt can load into DuckDB:

ResourceEndpointMethodData selectorDescription
accounts/accountsGETRetrieve a list of user accounts
transactions/transactionsGETRetrieve a list of historical transactions
accounting_categories/accounting-categoriesGETRetrieve a list of accounting categories
label_groups/label-groupsGETRetrieve a list of label groups
tax_rates/tax-ratesGETRetrieve a list of tax rates

How do I load only new Revolut records?

Revolut exposes created_at on transactions, 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.

{"name": "transactions", "endpoint": { "path": "transactions", "incremental": {"cursor_path": "created_at", "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 Revolut pipeline look like?

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

import dlt from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources @dlt.source def revolut_source(access_token=dlt.secrets.value): config: RESTAPIConfig = { "client": { "base_url": "https://b2b.revolut.com/api/1.0", "auth": {"type": "bearer", "token": access_token}, }, "resources": [ {"name": "transactions", "endpoint": {"path": "transactions"}}, {"name": "accounting_categories", "endpoint": {"path": "accounting-categories"}} ], } yield from rest_api_resources(config) def load_revolut_to_duckdb() -> None: pipeline = dlt.pipeline( pipeline_name="revolut_pipeline", destination="duckdb", dataset_name="revolut_data", ) load_info = pipeline.run(revolut_source()) print(load_info) if __name__ == "__main__": load_revolut_to_duckdb()

Run it with python revolut_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 Revolut 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):

import dlt data = dlt.pipeline("revolut_pipeline").dataset() df = data.transactions.df() print(df.head())

SQL:

SELECT * FROM revolut_data.transactions LIMIT 10;

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


How do I deploy the Revolut 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 Revolut 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 Revolut data to?

dlt loads into any of these — only the destination argument changes:

DestinationExample value
PostgreSQL"postgres"
BigQuery"bigquery"
Snowflake"snowflake"
Redshift"redshift"
Databricks"databricks"
Filesystem (S3, GCS, Azure)"filesystem"

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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