Load Lightning Engineering data in Python using dltHub

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

In this guide, we'll set up a complete Lightning Network Daemon (LND) 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 lnd_migration_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://lightning.engineering/api-docs/api/lnd/v1/",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            fuzz,,accounts,,peers
            ],
    }
    [...]
    yield from rest_api_resources(config)


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

  • endpoints: Various key functionalities of the LND API, including channels, payments, invoices, and wallet management.
  • macaroon: Used for RPC authentication, managing permissions for API access.
  • accounts: Handling account-related operations within the LND service.
  • watchtower: Enabling watchtower functionality for enhanced security and reliability.

You will then debug the Lightning Network Daemon (LND) 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 Lightning Network Daemon (LND) support.

    dlt init dlthub:lnd_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 Lightning Network Daemon (LND) API, as specified in @lnd_migration-docs.yaml 
Start with endpoints fuzz and  and skip incremental loading for now. 
Place the code in lnd_migration_pipeline.py and name the pipeline lnd_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 lnd_migration_pipeline.py and await further instructions.

  3. 🔒 Set up credentials

    The Lightning Network Daemon (LND) employs OAuth2 for authentication, specifically using bearer tokens and macaroons for RPC calls. This ensures secure access to its various endpoints.

    To get the appropriate API keys, please visit the original source at https://lightning.engineering/. 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 lnd_migration_pipeline.py

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

    Pipeline lnd_migration load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset lnd_migration_data
The duckdb destination used duckdb:/lnd_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 lnd_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("lnd_migration_pipeline").dataset()
# get uz table as Pandas frame
data.uz.df().head()

Running into errors?

It is crucial to establish a TLS/SSL connection and utilize macaroons for RPC authentication. Users should also ensure that LND is properly configured and synced to the blockchain network to avoid unexpected failures. Additionally, various endpoints may require specific macaroon permissions, which must be managed carefully to prevent unauthorized access.

Extra resources:

Next steps