Load OneBrick data in Python using dltHub

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

In this guide, we'll set up a complete Brick 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 brick_migration_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://sandbox.onebrick.io/v1/",
            {'auth': {'type': 'basic', 'token': 'client_id:client_secret'}},
        },
        "resources": [
            open, qr, ledger
        ],
    }
    [...]
    yield from rest_api_resources(config)


def get_data() -> None:
    # Connect to destination
    pipeline = dlt.pipeline(
        pipeline_name='brick_migration_pipeline',
        destination='duckdb',
        dataset_name='brick_migration_data', 
    )
    # Load the data
    load_info = pipeline.run(brick_migration_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 brick_migration’s API and loads it into Iceberg, DataFrames, files, or a database of your choice. Here are some of the endpoints you can load:

Open: Access the open endpoint for general information.
QRIS: Manage Quick Response Code Indonesia Standard transactions.
Ledger: Access ledger-related information.
E-Wallet: Interact with e-wallet services.
Balance: Retrieve balance information.
Payment Link: Create and manage payment links.
Disbursement: Handle disbursement actions.
Virtual Account: Manage virtual account operations.

You will then debug the Brick 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:

We suggest using a model like Claude 3.7 Sonnet or better
Index the REST API Source tutorial: https://dlthub.com/docs/dlt-ecosystem/verified-sources/rest_api/ and add it to context as @dlt rest api
Read our full steps on setting up Cursor

Now you're ready to get started!

⚙️ Set up dlt Workspace

Install dlt with duckdb support:
```
pip install "dlt[workspace]"
```
Initialize a dlt pipeline with Brick support.
```
dlt init dlthub:brick_migration duckdb
```
The init command will setup the necessary files and folders for the next step.

🤠 Start LLM-assisted coding

Here’s a prompt to get you started:

Prompt
Please generate a REST API Source for Brick API, as specified in @brick_migration-docs.yaml 
Start with endpoints open and  and skip incremental loading for now. 
Place the code in brick_migration_pipeline.py and name the pipeline brick_migration_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 brick_migration_pipeline.py and await further instructions.

🔒 Set up credentials

The API uses basic authentication, requiring a client ID and client secret to be sent in the headers as 'Authorization'.

To get the appropriate API keys, please visit the original source at https://www.onebrick.io/. If you want to protect your environment secrets in a production environment, look into setting up credentials with dlt.

🏃‍♀️ Run the pipeline in the Python terminal in Cursor


python brick_migration_pipeline.py

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


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

📈 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 brick_migration_pipeline show
```
🐍 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("brick_migration_pipeline").dataset()
# get pe table as Pandas frame
data.pe.df().head()
```

Running into errors?

Keep in mind that credentials differ between environments (sandbox and production). The sandbox environment allows testing without real transactions, but all actions are simulated. Make sure to validate requests in your backend system and not expose sensitive credentials client-side. Additionally, public access tokens are valid for only one time and for five minutes.

Extra resources:

Learn more with our 1h LLM-assisted coding course!

Next steps

How to deploy a pipeline
How to explore your data in marimo Notebooks
How to query your data in Python with dataset
[How to create REST API Sources with Cursor](https://dlthub.com/docs/dlt-ecosystem/llm-tooling/cursor-restapi "resources": [ open, qr, ledger ], } [...] yield from rest_api_resources(config)

def get_data() -> None: # Connect to destination pipeline = dlt.pipeline( pipeline_name='brick_migration_pipeline', destination='duckdb', dataset_name='brick_migration_data', ) # Load the data load_info = pipeline.run(brick_migration_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 brick_migration’s API and loads it into Iceberg, DataFrames, files, or a database of your choice. Here are some of the endpoints you can load:

- Open: Access the open endpoint for general information.
- QRIS: Manage Quick Response Code Indonesia Standard transactions.
- Ledger: Access ledger-related information.
- E-Wallet: Interact with e-wallet services.
- Balance: Retrieve balance information.
- Payment Link: Create and manage payment links.
- Disbursement: Handle disbursement actions.
- Virtual Account: Manage virtual account operations.

You will then debug the Brick 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

```default
Before getting started, let's make sure Cursor is set up correctly:
   - We suggest using a model like Claude 3.7 Sonnet or better
   - Index the REST API Source tutorial: https://dlthub.com/docs/dlt-ecosystem/verified-sources/rest_api/ and add it to context as **@dlt rest api**
   - [Read our full steps on setting up Cursor](https://dlthub.com/docs/dlt-ecosystem/llm-tooling/cursor-restapi#23-configuring-cursor-with-documentation)