Load Edge Delta data in Python using dltHub

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

In this guide, we'll set up a complete Edge Delta 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 edge_delta_migrations_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.edgedelta.com/",
            "auth": {
                "type": "apikey",
                "location": "header",
                "header_name": "X-ED-API-Token",
                "token": access_token,
            },
        },
        "resources": [
            "logs",
            "nodes",
            "usage"
            ],
    }
    [...]
    yield from rest_api_resources(config)


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

  • Pipeline Management: Configuration and deployment of data processing pipelines
  • Data Sources: Input nodes for collecting logs, metrics, and traces from various sources
  • Data Destinations: Output nodes for sending processed data to various platforms
  • Processors: Data transformation and processing nodes for filtering, parsing, and enriching data
  • Authentication & Authorization: API tokens, organization management, and user access control
  • Monitoring & Health: Agent health monitoring, diagnostics, and system metrics
  • Search & Analytics: Log search, metrics exploration, and pattern analysis

You will then debug the Edge Delta 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 Edge Delta support.

    dlt init dlthub:edge_delta_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 Edge Delta API, as specified in @edge_delta_migrations-docs.yaml 
Start with endpoints "logs" and "nodes" and skip incremental loading for now. 
Place the code in edge_delta_migrations_pipeline.py and name the pipeline edge_delta_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 edge_delta_migrations_pipeline.py and await further instructions.

  3. 🔒 Set up credentials

    Edge Delta uses API key authentication with a custom header. The API requires an API Token and Organization ID for all requests. The API key is passed via the X-ED-API-Token header. Both the API Token and Organization ID are required for API calls, with the Organization ID listed on the API Tokens page in the Edge Delta interface.

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

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

    Pipeline edge_delta_migrations load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset edge_delta_migrations_data
The duckdb destination used duckdb:/edge_delta_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 edge_delta_migrations_pipeline show --dashboard

  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("edge_delta_migrations_pipeline").dataset()
# get logs table as Pandas frame
data.logs.df().head()

Running into errors?

Edge Delta is primarily a data processing pipeline platform rather than a traditional REST API. The platform focuses on agent-based data collection and processing with complex configuration requirements. API tokens require both the token and Organization ID for authentication. The platform has specific networking requirements including outbound access to port 443 for api.edgedelta.com. Some features like eBPF traces are limited to Kubernetes environments and require specific kernel versions and privileges.

Extra resources:

Next steps