Load Oracle Common Application Components data in Python using dltHub

Build a Oracle Common Application Components-to-database or-dataframe pipeline in Python using dlt with automatic Cursor support.

In this guide, we'll set up a complete Department of Justice 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 justice_department_migrations_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://www.justice.gov/",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            ada, psn, afp
            ],
    }
    [...]
    yield from rest_api_resources(config)


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

  • General Information: Access to various sections and offices within the Department of Justice.
  • Civil Rights: Endpoints related to civil rights initiatives and resources.
  • News and Media: Access to news releases and media resources.
  • Forms and Applications: Various forms and application processes for DOJ services.

You will then debug the Department of Justice 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 Department of Justice support.

    dlt init dlthub:justice_department_migrations 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 Department of Justice API, as specified in @justice_department_migrations-docs.yaml 
Start with endpoints ada and psn and skip incremental loading for now. 
Place the code in justice_department_migrations_pipeline.py and name the pipeline justice_department_migrations_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 justice_department_migrations_pipeline.py and await further instructions.

  3. 🔒 Set up credentials

    Authentication is handled via an API key, which must be included in the request headers for authorization.

    To get the appropriate API keys, please visit the original source at https://www.justice.gov/. 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 justice_department_migrations_pipeline.py

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

    Pipeline justice_department_migrations load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset justice_department_migrations_data
The duckdb destination used duckdb:/justice_department_migrations.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 justice_department_migrations_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("justice_department_migrations_pipeline").dataset()
# get d table as Pandas frame
data.d.df().head()

Running into errors?

The API is not regularly updated, and some content may be outdated. Pagination is necessary for larger datasets, and it's mandatory to include a version number in every API call. Throttling may be required if request limits are exceeded.

Extra resources:

Next steps