Load WINK Streaming data in Python using dltHub

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

In this guide, we'll set up a complete WINK Camera Studio 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 wink_camera_migrations_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://10.130.90.188/api/",
            "auth": {
                "type": "session-based",
                "user": 'apiuser',
                "pass": 'apipass',
                "key": 'shared_key',
            },
        },
        "resources": [
            log, camera, video_feeds
            ],
    }
    [...]
    yield from rest_api_resources(config)


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

  • log: Access the log information.
  • camera: Manage camera settings and operations.
  • video_feeds: Manage and monitor video feeds.
  • event_monitoring: Monitor events from the system.
  • camera_management: Handle camera configurations and controls.

You will then debug the WINK Camera Studio 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 WINK Camera Studio support.

    dlt init dlthub:wink_camera_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 WINK Camera Studio API, as specified in @wink_camera_migrations-docs.yaml 
Start with endpoints log and  and skip incremental loading for now. 
Place the code in wink_camera_migrations_pipeline.py and name the pipeline wink_camera_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 wink_camera_migrations_pipeline.py and await further instructions.

  3. 🔒 Set up credentials

    This source utilizes session-based authentication, requiring an XML format for credentials, and includes a user, password, and shared key for access.

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

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

    Pipeline wink_camera_migrations load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset wink_camera_migrations_data
The duckdb destination used duckdb:/wink_camera_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 wink_camera_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("wink_camera_migrations_pipeline").dataset()
# get o table as Pandas frame
data.o.df().head()

Running into errors?

All API interactions necessitate SSL/HTTPS. It's crucial to perform tests in non-production environments first and to monitor API response times. Major version changes may disrupt functionality, so verify compatibility before firmware updates. Proper error handling and logging are recommended for all API access.

Extra resources:

Next steps