Load Apache Geode data in Python using dltHub

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

In this guide, we'll set up a complete Apache Geode 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 apache_geode_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://geode.apache.org/geode/v1/",
            "auth": {
                "type": "bearer",
                "token": "encoded_token_string",
            },
        },
        "resources": [
            functions, ping
        ],
    }
    [...]
    yield from rest_api_resources(config)


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

Region Management: Create, retrieve, update, and delete data regions and their contents by key
Query Operations: Execute ad-hoc OQL queries, manage saved queries, and retrieve query results
Function Execution: List and invoke pre-defined functions on the server
Server Information: Get server status, health checks, and cluster node details
Health & Diagnostics: Ping endpoint for server availability and system status checks

You will then debug the Apache Geode 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:

We suggest using a model like Claude 3.7 Sonnet or better
Index the REST API Source tutorial: https://dlthub.com/docs/dlt-ecosystem/verified-sources/rest_api/ and add it to context as @dlt rest api
Read our full steps on setting up Cursor

Now you're ready to get started!

⚙️ Set up dlt Workspace

Install dlt with duckdb support:
```
pip install dlt[workspace]
```
Initialize a dlt pipeline with Apache Geode support.
```
dlt init dlthub:apache_geode duckdb
```
The init command will setup the necessary files and folders for the next step.

🤠 Start LLM-assisted coding

Here’s a prompt to get you started:

Prompt
Please generate a REST API Source for Apache Geode API, as specified in @apache_geode-docs.yaml 
Start with endpoint(s) functions and ping and skip incremental loading for now. 
Place the code in apache_geode_pipeline.py and name the pipeline apache_geode_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 apache_geode_pipeline.py and await further instructions.

🔒 Set up credentials

REST API requires authentication on each request using either basic credentials or token-based authentication. Include security-username and security-password headers for basic auth, or use Authorization: Bearer [encoded token-string] header for OAuth token-based security. Authentication is validated against stored username-password pairs via a SecurityManager interface implementation.

To get the appropriate API keys, please visit the original source at geode.apache.org. If you want to protect your environment secrets in a production environment, look into setting up credentials with dlt.

🏃‍♀️ Run the pipeline in the Python terminal in Cursor


python apache_geode_pipeline.py

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


Pipeline apache_geode load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset apache_geode_data
The duckdb destination used duckdb:/apache_geode.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

📈 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 apache_geode_pipeline show 
```
🐍 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("apache_geode_pipeline").dataset()
# get functions table as Pandas frame
data.functions.df().head()
```

Extra resources:

Learn more with our 1h LLM-assisted coding course!