Load Twilio data in Python using dltHub

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

In this guide, we'll set up a complete Twilio 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 twilio_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.twilio.com/2010-04-01/",
            "auth": {
                "type": "basic",
                "username": AccountSid,
                "password": AuthToken,
            },
        },
        "resources": [
            "accounts",
            "calls",
            "messages"
            ],
    }
    [...]
    yield from rest_api_resources(config)


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

  • Accounts: Access and manage Twilio accounts.
  • Calls: Manage and retrieve information about calls.
  • Messages: Send and receive SMS and MMS messages.
  • Conferences: Handle call conferences and participants.
  • Conversations: Manage real-time messaging and participants.

You will then debug the Twilio 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 Twilio support.

    dlt init dlthub:twilio 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 Twilio API, as specified in @twilio-docs.yaml 
Start with endpoints "accounts" and "calls" and skip incremental loading for now. 
Place the code in twilio_pipeline.py and name the pipeline twilio_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 twilio_pipeline.py and await further instructions.

  3. 🔒 Set up credentials

    Twilio uses HTTP Basic authentication, requiring an Account SID and Auth Token to authenticate requests.

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

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

    Pipeline twilio load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset twilio_data
The duckdb destination used duckdb:/twilio.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 twilio_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("twilio_pipeline").dataset()
# get "accounts" table as Pandas frame
data.accounts.df().head()

Running into errors?

Be aware that Twilio's endpoints may have rate limits, and it's important to handle errors such as '401 Unauthorized' and '429 Too Many Requests' properly. Implementing exponential backoff for rate limits is recommended.

Extra resources:

Next steps