Load Local Contexts data in Python using dltHub

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

In this guide, we'll set up a complete Local Contexts 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 local_contexts_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://localcontextshub.org/api/",
            "auth": {
                "type": "api_key",
                "key": "api_key",
            },
        },
        "resources": [
            GET, GET
        ],
    }
    [...]
    yield from rest_api_resources(config)


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

  • GET endpoints: Retrieve data from the Local Contexts Hub API for accessing information about Indigenous data governance, labels, and community resources
  • API base operations: Core API functionality at the root endpoint for general API interactions and status checks
  • Sandbox environment: Testing and development endpoints available in the sandbox version for safe experimentation before production use

You will then debug the Local Contexts 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 Local Contexts support.

    dlt init dlthub:local_contexts 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 Local Contexts API, as specified in @local_contexts-docs.yaml 
Start with endpoint(s) GET and GET and skip incremental loading for now. 
Place the code in local_contexts_pipeline.py and name the pipeline local_contexts_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 local_contexts_pipeline.py and await further instructions.

  3. 🔒 Set up credentials

    An API key from your Hub account is required to authenticate to v2 of the API. The API key must be provided to access endpoints, with access levels and accessible Projects varying based on the account type (Subscribed Institution, Subscribed Researcher, Confirmed Community, Certified Integration Partner, or Unsubscribed/Unauthorized).

However, the documentation does not specify the exact header name, parameter name, or method for transmitting the API key in requests. To use this API from Python, you would need to contact support@localcontexts.org for details on how to include the API key in your requests (e.g., as an Authorization header, X-API-Key header, query parameter, etc.).

To get the appropriate API keys, please visit the original source at localcontexts.org.
If you want to protect your environment secrets in a production environment, look into [setting up credentials with dlt](https://dlthub.com/docs/walkthroughs/add_credentials).

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

```shell
python local_contexts_pipeline.py
```

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

```shell
Pipeline local_contexts load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset local_contexts_data
The duckdb destination used duckdb:/local_contexts.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

```shell
dlt pipeline local_contexts_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.


```python
import dlt

data = dlt.pipeline("local_contexts_pipeline").dataset()

get GET table as Pandas frame

data.GET.df().head() ```

Extra resources:

Next steps