Load Cognigy data in Python using dltHub

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

In this guide, we'll set up a complete Cognigy 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 cognigy_migration_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.cognigy.com/v1/",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            api/keys,,ai/agents,,insights/reports/overview
            ],
    }
    [...]
    yield from rest_api_resources(config)


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

  • api: Contains various API interactions such as keys and personas.
  • ai: Involves AI agents and their management, including deployment and analysis.
  • insights: Focuses on reporting and insights related to agent performance and engagement.
  • live-agent: Includes endpoints for managing live agent interactions.

You will then debug the Cognigy 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 Cognigy support.

    dlt init dlthub:cognigy_migration 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 Cognigy API, as specified in @cognigy_migration-docs.yaml 
Start with endpoints api/keys and  and skip incremental loading for now. 
Place the code in cognigy_migration_pipeline.py and name the pipeline cognigy_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 cognigy_migration_pipeline.py and await further instructions.

  3. 🔒 Set up credentials

    Cognigy uses OAuth2 for authentication, specifically utilizing a refresh token flow. Necessary client credentials must be set up within a connected app, and the access token is supplied in the header for authorization.

    To get the appropriate API keys, please visit the original source at https://www.cognigy.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 cognigy_migration_pipeline.py

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

    Pipeline cognigy_migration load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset cognigy_migration_data
The duckdb destination used duckdb:/cognigy_migration.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 cognigy_migration_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("cognigy_migration_pipeline").dataset()
# get pi/key table as Pandas frame
data.pi/key.df().head()

Running into errors?

It's essential to set up a connected app for OAuth2 authentication. Some providers may need manual settings adjustments, and there are strict limits on API call quotas. Be aware of deprecated features and ensure compatibility between versions when importing packages. Proper security measures are necessary to protect against XSS attacks.

Extra resources:

Next steps