Load Sigma Computing data in Python using dltHub

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

In this guide, we'll set up a complete Sigma Computing 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 sigma_computing_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://your-instance.api.sigmacomputing.com/api/v1/",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            "files",
            "connections",
            "datasets"
            ],
    }
    [...]
    yield from rest_api_resources(config)


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

  • Files: Retrieve and manage files in the Sigma environment.
  • Connections: Manage data connections within Sigma.
  • Datasets: Access and manipulate datasets.
  • Members: Manage users and their permissions.
  • Teams: Organize members into teams for collaboration.
  • Templates: Use predefined templates for reports or dashboards.
  • Workspaces: Manage workspace settings and resources.
  • Workbooks: Access and manage workbooks for data analysis.

You will then debug the Sigma Computing 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 Sigma Computing support.

    dlt init dlthub:sigma_computing 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 Sigma Computing API, as specified in @sigma_computing-docs.yaml 
Start with endpoints "files" and "connections" and skip incremental loading for now. 
Place the code in sigma_computing_pipeline.py and name the pipeline sigma_computing_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 sigma_computing_pipeline.py and await further instructions.

  3. 🔒 Set up credentials

    The authentication method utilized is OAuth2 with refresh tokens, which necessitates setting up a connected app in Sigma.

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

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

    Pipeline sigma_computing load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset sigma_computing_data
The duckdb destination used duckdb:/sigma_computing.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 sigma_computing_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("sigma_computing_pipeline").dataset()
# get "files" table as Pandas frame
data.files.df().head()

Running into errors?

It is critical to ensure that OAuth credentials are correct to avoid '401 Unauthorized' errors. Additionally, the API has a rate limit that may lead to '429 Too Many Requests' errors if calls are made too frequently. Pagination is handled using the DefaultPaginator across all endpoints, which should be considered when retrieving large sets of data.

Extra resources:

Next steps