Load Observe data in Python using dltHub

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

In this guide, we'll set up a complete Observe 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 observe_api_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://<customerid>.<observe-domain>.com/v1/",
            "auth": {
                "type": "bearer",
                "token": "api_token",
            },
        },
        "resources": [
            meta/reftable, monitor-mute-rules
        ],
    }
    [...]
    yield from rest_api_resources(config)


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

Metadata & Reference Tables: Manage reference tables and metadata with create, read, update, delete operations
Monitors: Create, configure, and delete monitoring rules and alert conditions
Monitor Mute Rules: Manage muting policies to suppress alerts during maintenance windows
Service Accounts: Administer service account creation, deletion, and lifecycle management
API Tokens: Generate, manage, and revoke authentication tokens for service accounts
Authentication: Handle login requests and polling for authentication status

You will then debug the Observe 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 Observe support.
```
dlt init dlthub:observe_api 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 Observe API, as specified in @observe_api-docs.yaml 
Start with endpoint(s) meta/reftable and monitor-mute-rules and skip incremental loading for now. 
Place the code in observe_api_pipeline.py and name the pipeline observe_api_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 observe_api_pipeline.py and await further instructions.

🔒 Set up credentials

API tokens are used for authentication. Create tokens via POST /v1/service-accounts/{accountId}/api-tokens endpoint, then include the token in requests. Tokens are managed through service accounts and can be created, retrieved, patched, and deleted via dedicated API endpoints.

To get the appropriate API keys, please visit the original source at developer.observeinc.com. 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 observe_api_pipeline.py

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


Pipeline observe_api load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset observe_api_data
The duckdb destination used duckdb:/observe_api.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 observe_api_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("observe_api_pipeline").dataset()
# get ["meta/reftable"] table as Pandas frame
data.["meta/reftable"].df().head()
```

Extra resources:

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