Load Bitso data in Python using dltHub

Build a Bitso-to-database or-dataframe pipeline in Python using dlt with automatic Cursor support.

In this guide, we'll set up a complete Bitso data pipeline from API credentials to your first data load in just 10 minutes. You'll end up with a fully declarative Python pipeline based on dlt's REST API connector, like in the partial example code below:

Example code
@dlt.source
def bitso_currency_dictionary_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://stage.bitso.com/api/v3/payments/usd/bridge/money-transmitters/customers",
            "auth": {
                "type": "bearer",
                "token": access_token,
            }
        },
        "resources": [
            "terms-of-service", "businesses"
        ],
    }
    [...]
    yield from rest_api_resources(config)


def get_data() -> None:
    # Connect to destination
    pipeline = dlt.pipeline(
        pipeline_name='bitso_currency_dictionary_pipeline',
        destination='duckdb',
        dataset_name='bitso_currency_dictionary_data', 
    )
    # Load the data
    load_info = pipeline.run(bitso_currency_dictionary_source())
    print(load_info)

Why use dltHub Workspace with LLM Context to generate Python pipelines?

  • Accelerate pipeline development with AI-native context
  • Debug pipelines, validate schemas and data with the integrated Pipeline Dashboard
  • Build Python notebooks for end users of your data
  • Low maintenance thanks to Schema evolution with type inference, resilience and self documenting REST API connectors. A shallow learning curve makes the pipeline easy to extend by any team member
  • dlt is the tool of choice for Pythonic Iceberg Lakehouses, bringing mature data loading to pythonic Iceberg with or without catalogs

What you’ll do

We’ll show you how to generate a readable and easily maintainable Python script that fetches data from bitso_currency_dictionary’s API and loads it into Iceberg, DataFrames, files, or a database of your choice. Here are some of the endpoints you can load:

  • Webhook Endpoints: Endpoints related to webhook configurations and events.

    • /spei/v1/webhooks: Manages webhook settings for notifications.

  • Withdrawal Endpoints: Endpoints to manage and retrieve information about withdrawals.

    • /spei/v1/withdrawals?only_refunds=true: Retrieves only the refund withdrawals.
    • /api/v3/withdrawals/{wid}: Fetches details of a specific withdrawal by ID.
    • /api/v3/withdrawals: General endpoint to manage all withdrawals.

  • Deposit Endpoints: Endpoints associated with deposit operations.

    • /spei/test/deposits: Test endpoint for handling deposits.

  • Payment Endpoints: Endpoints for managing and querying payment transactions.

    • /spei/v1/payments?end_date=2024-11-19&start_date=2024-08-01&status=COMPLETED: Retrieves completed payments within a specific date range.
    • /spei/v1/payments?end_date=2024-11-19&start_date=2024-08-01&only_rejections=true: Retrieves only rejected payments within a specific date range.
    • /spei/v1/payments: General endpoint for managing payments.

  • Balance Endpoints: Endpoint for checking account balances.

    • /api/v3/balance?currency=mxn: Retrieves the balance for a specified currency (Mexican Peso).

  • Clabe Endpoints: Endpoints related to CLABE (Clabe is a standard for bank account identification in Mexico).

    • /spei/v1/clabes: General endpoint for managing CLABE information.
    • /spei/v1/clabes/{clabe}: Retrieves specific information related to a given CLABE.

You will then debug the Bitso pipeline using our Pipeline Dashboard tool to ensure it is copying the data correctly, before building a Notebook to explore your data and build reports.

Setup & steps to follow

💡
Before getting started, let's make sure Cursor is set up correctly:

Now you're ready to get started!

  1. ⚙️ Set up dlt Workspace

    Install dlt with duckdb support:

    pip install "dlt[workspace]"

    Initialize a dlt pipeline with Bitso support.

    dlt init dlthub:bitso_currency_dictionary duckdb

    The init command will setup the necessary files and folders for the next step.

  2. 🤠 Start LLM-assisted coding

    Here’s a prompt to get you started:

    Prompt
    Please generate a REST API Source for Bitso API, as specified in @bitso_currency_dictionary-docs.yaml 
Start with endpoints terms-of-service and businesses and skip incremental loading for now. 
Place the code in bitso_currency_dictionary_pipeline.py and name the pipeline bitso_currency_dictionary_pipeline. 
If the file exists, use it as a starting point. 
Do not add or modify any other files. 
Use @dlt rest api as a tutorial. 
After adding the endpoints, allow the user to run the pipeline with python bitso_currency_dictionary_pipeline.py and await further instructions.

  3. 🔒 Set up credentials

    The snippets contain references to authentication documentation for Bitso's API, specifically about creating signed requests and understanding their auth mechanism, but do not include specific details on required keys, tokens, client IDs, client secrets, or any other authentication parameters.

    To get the appropriate API keys, please visit the original source at https://docs.bitso.com/bitso-api/docs/currency-dictionary. If you want to protect your environment secrets in a production environment, look into setting up credentials with dlt.

  4. 🏃‍♀️ Run the pipeline in the Python terminal in Cursor

    python bitso_currency_dictionary_pipeline.py

    If your pipeline runs correctly, you’ll see something like the following:

    Pipeline bitso_currency_dictionary load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset bitso_currency_dictionary_data
The duckdb destination used duckdb:/bitso_currency_dictionary.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

  5. 📈 Debug your pipeline and data with the Pipeline Dashboard

    Now that you have a running pipeline, you need to make sure it’s correct, so you do not introduce silent failures like misconfigured pagination or incremental loading errors. By launching the dlt Workspace Pipeline Dashboard, you can see various information about the pipeline to enable you to test it. Here you can see:

    • Pipeline overview: State, load metrics
    • Data’s schema: tables, columns, types, hints
    • You can query the data itself
    dlt pipeline bitso_currency_dictionary_pipeline show

  6. 🐍 Build a Notebook with data explorations and reports

    With the pipeline and data partially validated, you can continue with custom data explorations and reports. To get started, paste the snippet below into a new marimo Notebook and ask your LLM to go from there. Jupyter Notebooks and regular Python scripts are supported as well.

    import dlt

data = dlt.pipeline("bitso_currency_dictionary_pipeline").dataset()
# get "terms-of-service" table as Pandas frame
data."terms-of-service".df().head()

Extra resources:

Next steps