Load Transmit Security data in Python using dltHub

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

In this guide, we'll set up a complete Transmit Security 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 transmit_security_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.transmitsecurity.io/cis/v1/delegated-access/consents/me/permissions",
            "auth": {
                "type": "bearer",
                "token": access_token,
            }
        },
        "resources": [
            "actor", "subject"
        ],
    }
    [...]
    yield from rest_api_resources(config)


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

  • Event Endpoints:

    • /apisec/v1/api/events/endpoints: Retrieve a list of event endpoints with a specified limit.
    • /apisec/v1/api/events: Fetch events based on various filters including date range, order, and user agent.

  • OIDC Token Endpoints:

    • /oidc/token: Endpoint to obtain an OIDC token for authentication purposes.

  • Consent and Delegated Access:

    • /cis/auth/consent/callback: Callback endpoint for handling consent requests.
    • /cis/v1/delegated-access/consents/me/request: Request for delegated access consent.
    • /cis/v1/delegated-access/consents/me/grant: Grant consent for delegated access.

  • Authentication Endpoints:

    • /cis/v1/auth/otp/send: Send a One-Time Password for authentication.
    • /cis/v1/users/me/totp/revoke: Revoke Time-based One-Time Password (TOTP) for the current user.
    • /cis/v1/auth/webauthn/cross-device/status: Check the status of cross-device WebAuthn authentication.

  • User Management:

    • /cis/v1/users/{userId}: Manage user details by user ID.
    • /cis/v1/auth/users/{userId}: Perform actions related to a specific user by user ID.

  • Single Sign-On (SSO) Service:

    • /cis/v1/sso-service/sso-group: Manage SSO groups.
    • /cis/v1/sso-service/sso-group/{groupId}: Manage specific SSO group by group ID.

  • Logout Endpoint:

    • /cis/v1/auth/logout: Endpoint to log out the current user.

  • Social Authentication:

    • /cis/v1/auth/line: Authenticate users via Line.
    • /cis/v1/auth/apple: Authenticate users via Apple.

You will then debug the Transmit Security 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 Transmit Security support.

    dlt init dlthub:transmit_security 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 Transmit Security API, as specified in @transmit_security-docs.yaml 
Start with endpoints actor and subject and skip incremental loading for now. 
Place the code in transmit_security_pipeline.py and name the pipeline transmit_security_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 transmit_security_pipeline.py and await further instructions.

  3. 🔒 Set up credentials

    To authenticate, you need a token which you can obtain from the Authorization section, and you must apply it by including it in the header with the name 'Authorization', formatted as 'Bearer <YOUR_JWT_HERE>'.

    To get the appropriate API keys, please visit the original source at https://developer.transmitsecurity.com/openapi/api-security/api-security/. 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 transmit_security_pipeline.py

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

    Pipeline transmit_security load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset transmit_security_data
The duckdb destination used duckdb:/transmit_security.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 transmit_security_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("transmit_security_pipeline").dataset()
# get "actor" table as Pandas frame
data."actor".df().head()

Extra resources:

Next steps