Load Squirro data in Python using dltHub

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

In this guide, we'll set up a complete Squirro 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 squirro_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://localhost:9200/_nodes/stats",
            "auth": {
                "type": "bearer",
                "token": access_token,
            }
        },
        "resources": [
            "os", "indices"
        ],
    }
    [...]
    yield from rest_api_resources(config)


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

  • Snapshot Repository: Endpoints related to managing and interacting with snapshot repositories for data backup.

    • /_snapshot/my_repository: Access or manage the specified snapshot repository.

  • Node Information: Endpoints that provide details about the nodes in the cluster.

    • /_nodes?pretty: Get a list of nodes with detailed information in a readable format.
    • /_nodes/stats?pretty: Retrieve statistics about the nodes in a friendly format.
    • /_nodes/stats/jvm?pretty: Get JVM statistics for each node in the cluster.
    • /_nodes/stats/fs?pretty: Access file system statistics for the nodes.

  • Cluster Settings and Health: Endpoints for managing cluster settings and health checks.

    • /_cluster/settings: View or modify cluster-wide settings.
    • /_cluster/health?pretty=true: Check the health status of the cluster in a readable format.
    • /_cluster/reroute?retry_failed: Reroute shards that have failed to be allocated.

  • Shards and Indices: Endpoints that provide information about shards and indices in the cluster.

    • /_cat/shards?v: List all shards in the cluster with verbose output.
    • /_cat/shards: Get a concise list of all shards.
    • /_cat/indices?v: Display information about all indices in the cluster.

  • Template Management: Endpoints related to index templates and component templates.

    • /_index_template/squirro_v9?pretty: Access or manage a specific index template.
    • /_index_template?pretty: Retrieve all index templates in a readable format.
    • /_component_template/metadata_property: Manage component templates related to metadata properties.

  • Settings and Stats: Endpoints for retrieving settings and statistics of various components.

    • /_settings: Access settings for various indices or the cluster.
    • /_stats?pretty: Get overall statistics for indices in a readable format.
    • /_nodes/stats/indices/fielddata?pretty: Retrieve field data statistics for indices in a friendly format.

You will then debug the Squirro 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 Squirro support.

    dlt init dlthub:squirro 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 Squirro API, as specified in @squirro-docs.yaml 
Start with endpoints os and indices and skip incremental loading for now. 
Place the code in squirro_pipeline.py and name the pipeline squirro_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 squirro_pipeline.py and await further instructions.

  3. 🔒 Set up credentials

    Auth information not found.

    To get the appropriate API keys, please visit the original source at https://docs.squirro.com/en/latest/technical/api/index.html. 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 squirro_pipeline.py

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

    Pipeline squirro load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset squirro_data
The duckdb destination used duckdb:/squirro.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 squirro_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("squirro_pipeline").dataset()
# get "os" table as Pandas frame
data."os".df().head()

Extra resources:

Next steps