Load Digicust data in Python using dltHub

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

In this guide, we'll set up a complete Digicust 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 digicust_migration_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://v2.digicust.com/",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            solutions/reports/,solutions/ai-master-data/,solutions/ai-email-inbox/
            ],
    }
    [...]
    yield from rest_api_resources(config)


def get_data() -> None:
    # Connect to destination
    pipeline = dlt.pipeline(
        pipeline_name='digicust_migration_pipeline',
        destination='duckdb',
        dataset_name='digicust_migration_data', 
    )
    # Load the data
    load_info = pipeline.run(digicust_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 digicust_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:

Solutions Reports: Endpoints related to generating and retrieving reports.
AI Master Data: Endpoints for managing and utilizing AI-powered master data.
AI Email Inbox: Endpoints for handling AI-driven email processing.
BTI Management: Endpoints for managing Binding Tariff Information.
AI Customs Audit: Endpoints for conducting automated customs audits.
AI Export Control: Endpoints for managing export control processes.
AI Document Control: Endpoints for controlling and managing documents using AI.
AI Customs Declarations: Endpoints for automating customs declarations.
AI Tariff Classification: Endpoints for classifying goods according to tariffs.
Authorisation Management: Endpoints for managing authorisations in the customs process.
Supplier Declaration Management: Endpoints for managing supplier declarations.
Intelligent Document Processing: Endpoints for utilizing intelligent document processing capabilities.

You will then debug the Digicust 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 Digicust support.
```
dlt init dlthub:digicust_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 Digicust API, as specified in @digicust_migration-docs.yaml 
Start with endpoints solutions/reports/ and solutions/ai-master-data/ and skip incremental loading for now. 
Place the code in digicust_migration_pipeline.py and name the pipeline digicust_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 digicust_migration_pipeline.py and await further instructions.

🔒 Set up credentials

Digicust uses OAuth2 with a refresh token, requiring the setup of a connected app for authentication.

To get the appropriate API keys, please visit the original source at https://www.digicust.com/. 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 digicust_migration_pipeline.py

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


Pipeline digicust_migration load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset digicust_migration_data
The duckdb destination used duckdb:/digicust_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 digicust_migration_pipeline show --dashboard
```
🐍 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("digicust_migration_pipeline").dataset()
# get olutions/reports table as Pandas frame
data.olutions/reports.df().head()
```

Running into errors?

Digicust is currently in the process of obtaining ISO 27001 certification and is fully compliant with European data protection regulations. Users should be aware that the platform automates customs processes significantly, but it still requires proper setup and maintenance of connected applications.

Extra resources:

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