In this guide, we'll set up a complete Code::Stats 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 code_stats_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://codestats.net/api/",
            "auth": {
                "type": "api_key",
                "header": "X-API-Key",
                "token": api_key,
            },
        },
        "resources": [
            "users/username", "my/pulses"
            ],
    }
    [...]
    yield from rest_api_resources(config)


def get_data() -> None:
    # Connect to destination
    pipeline = dlt.pipeline(
        pipeline_name='code_stats_pipeline',
        destination='duckdb',
        dataset_name='code_stats_data', 
    )
    # Load the data
    load_info = pipeline.run(code_stats_source())
    print(load_info) 

Why use dlt 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 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 Code::Stats's API and loads it into Iceberg, DataFrames, files, or a database of your choice. Here are some of the endpoints you can load:

• User Data: Retrieve user profile information and statistics by username
• Pulse Tracking: View and record coding activity pulses, including GET requests to view pulses and POST requests to submit new pulse data
• Activity Logging: Submit individual coding activities and pulse records to track programming progress

You will then debug the Code::Stats 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, set up a virtual environment (instructions) and install the dlt workspace:
uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

Now you're ready to get started!

1. Install the dlt AI Workbench

   Configure the workbench for your coding assistant:

   Copy
   dlt ai init --agent <your-agent> # <agent>: claude | cursor | codex

   This installs project rules, a secrets management skill, appropriate ignore files, and configures the dlt MCP server for your agent.

   Learn more about the dltHub AI Workbench and setup details for each assistant →

2. Install the rest-api-pipeline toolkit

   The AI Workbench provides different toolkits for each phase of the data engineering lifecycle. To start you need to install the rest-api-pipeline toolkit:

   Copy
   dlt ai toolkit rest-api-pipeline install

   This loads different skills and contexts about dlt the agent uses to build the pipeline iteratively, efficiently, and safely. Importantly, it does not need to ask you for credentials directly. In dlt, API credentials are provided via a secrets.toml file (learn more about secrets management →), and the agent should use the MCP tools to see their shape and detect misconfigurations. It never needs to access the file directly.

   Learn more about the rest-api-pipeline toolkit →

3. Start LLM-assisted coding

   Here's a prompt to get you started:

   Prompt
   Copy
   Use /find-source to load data from the Code::Stats API into DuckDB.

   The AI Workbench rest-api-pipeline toolkit takes over from here — it reads relevant API documentation, presents you with options for which endpoints to load, and then follows a structured workflow to scaffold, debug, and validate the pipeline step by step.

4. View the result

   After the rest-api-pipeline workflow has finished, you will end up with a working REST API source with validated endpoints and a pipeline that writes data into a local dataset you have inspected and verified.

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

   By launching the Pipeline Dashboard, you can see various information about the pipeline and the loaded data

   • Pipeline overview: State, load metrics
   • Data's schema: tables, columns, types, hints
   • You can query the data itself

   Copy
   dlt pipeline code_stats_pipeline show

API Key Authentication

The Code::Stats API uses API key-based authentication. Include your API key in the X-API-Key header for all requests. The API key is a unique identifier tied to your user account and must be kept confidential.

To get the appropriate API keys, please visit the original source at codestats.net.
If you want to protect your environment secrets in a production environment, look into [setting up credentials with dlt](https://dlthub.com/docs/walkthroughs/add_credentials).

4. 🏃‍♀️ Run the pipeline in your terminal

```shell
python code_stats_pipeline.py
```

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

```shell
Pipeline code_stats load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset code_stats_data
The duckdb destination used duckdb:/code_stats.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 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

```shell
dlt pipeline code_stats_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.


```python
import dlt

dataset = dlt.pipeline("code_stats_pipeline").dataset()

Table name extrapolated from endpoint: [users/username]

Get [users_username] table as Pandas DataFrame

df = dataset.table("[users_username]").df().head() ```

Next steps

You can go to the next phases of your data engineering journey by handing over to other toolkits of the dltHub AI Workbench:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-runtime — Deploy, schedule, and monitor your pipeline in production

Copy
dlt ai toolkit data-exploration install
dlt ai toolkit dlthub-runtime install

Or explore the following resources for more information:

• How to deploy a pipeline
• How to explore your data in marimo Notebooks
• How to query your data in Python with dataset