Data enrichment part one: User-agent device data enrichment#

Data enrichment enhances raw data with valuable information from multiple sources, increasing its analytical and decision-making value.

This part covers enriching sample data with device price. Understanding the price segment of the device that the user used to access your service can be helpful in personalized marketing, customer segmentation, and more.

This documentation will discuss how to enrich user device information with the average market price.

Setup guide#

We use SerpAPI to retrieve device prices using Google Shopping, but alternative services or APIs are viable.

Creating a data enrichment pipeline#

You can either follow the example in the linked Colab notebook or follow this documentation to create the user-agent device data enrichment pipeline.

A. Colab notebook#

The Colab notebook combines three data enrichment processes for a sample dataset, starting with "Data enrichment part one: User-agent device data".

Here's the link to the notebook:
Colab Notebook.

B. Create a pipeline#

Alternatively, to create a data enrichment pipeline, you can start by creating the following directory structure:

user_device_enrichment/
├── .dlt/
│ └── secrets.toml
└── device_enrichment_pipeline.py

1. Creating resource#

dlt works on the principle of sources and resources.

This data resource yields data typical of what many web analytics and tracking tools can collect. However, the specifics of what data is collected and how it's used can vary significantly among different tracking services.

Let's examine a synthetic dataset created for this article. It includes:

user_id: Web trackers typically assign a unique ID to users for tracking their journeys and interactions over time.

device_name: User device information helps in understanding the user base's device preferences.

page_refer: The referer URL is tracked to analyze traffic sources and user navigation behavior.

Here's the resource that yields the sample data as discussed above:

import dlt

@dlt.resource(write_disposition="append")
def tracked_data():
    """
    A generator function that yields a series of dictionaries, each representing
    user tracking data.

    This function is decorated with `dlt.resource` to integrate into the DLT (Data
    Loading Tool) pipeline. The `write_disposition` parameter is set to "append" to
    ensure that data from this generator is appended to the existing data in the
    destination table.

    Yields:
        dict: A dictionary with keys 'user_id', 'device_name', and 'page_referer',
        representing the user's tracking data including their device and the page
        they were referred from.
    """

    # Sample data representing tracked user data
    sample_data = [
        {"user_id": 1, "device_name": "Sony Experia XZ", "page_referer":
        "https://b2venture.lightning.force.com/"},
        {"user_id": 2, "device_name": "Samsung Galaxy S23 Ultra 5G",
        "page_referer": "https://techcrunch.com/2023/07/20/can-dlthub-solve-the-python-library-problem-for-ai-dig-ventures-thinks-so/"},
        {"user_id": 3, "device_name": "Apple iPhone 14 Pro Max",
        "page_referer": "https://dlthub.com/success-stories/freelancers-perspective/"},
        {"user_id": 4, "device_name": "OnePlus 11R",
        "page_referer": "https://www.reddit.com/r/dataengineering/comments/173kp9o/ideas_for_data_validation_on_data_ingestion/"},
        {"user_id": 5, "device_name": "Google Pixel 7 Pro", "page_referer": "https://pypi.org/"},
    ]

    # Yielding each user's data as a dictionary
    for user_data in sample_data:
        yield user_data

2. Create `fetch_average_price` function#

This particular function retrieves the average price of a device by utilizing SerpAPI and Google shopping listings. To filter the data, the function uses dlt state, and only fetches prices from SerpAPI for devices that have not been updated in the most recent run or for those that were loaded more than 180 days in the past.

The first step is to register on SerpAPI and obtain the API token key.

In the .dlt folder, there's a file called secrets.toml. It's where you store sensitive information securely, like access tokens. Keep this file safe. Here's its format for service account authentication:
```
[sources]
api_key= "Please set me up!" # Serp Api key.
```
Replace the value of the api_key.

Create the fetch_average_price() function as follows:

import requests

# Uncomment transformer function if it is to be used as a transformer,
# otherwise, it is being used with the `add_map` functionality.

# @dlt.transformer(data_from=tracked_data)
def fetch_average_price(user_tracked_data):
    """
    Fetches the average price of a device from an external API and
    updates the user_data dictionary.

    This function retrieves the average price of a device specified in the
    user_data dictionary by making an API request. The price data is cached
    in the device_info state to reduce API calls. If the data for the device
    is older than 180 days, a new API request is made.

    Args:
        user_tracked_data (dict): A dictionary containing user data, including
        the device name.

    Returns:
        dict: The updated user_data dictionary with added device price and
        updated timestamp.
    """

    # Retrieve the API key from dlt secrets
    api_key: str = dlt.secrets.get("sources.api_key")

    # Get the current resource state for device information
    device_info = dlt.current.resource_state().setdefault("devices", {})

    # Current timestamp for checking the last update
    current_timestamp = datetime.datetime.now()

    # Print the current device information
    # print(device_info) # if you need to check state

    # Extract the device name from user data
    device = user_tracked_data['device_name']
    device_data = device_info.get(device, {})

    # Calculate the time since the last update
    last_updated = (
        current_timestamp -
        device_data.get('timestamp', datetime.datetime.min)
    )
    # Check if the device is not in state or data is older than 180 days
    if device not in device_info or last_updated > datetime.timedelta(days=180):
        try:
            # Make an API request to fetch device prices
            response = requests.get("https://serpapi.com/search", params={
                "engine": "google_shopping", "q": device,
                "api_key": api_key, "num": 10
            })
        except requests.RequestException as e:
            print(f"Request failed: {e}")
            return None

        if response.status_code != 200:
            print(f"Failed to retrieve data: {response.status_code}")
            return None

        # Process the response to extract prices
        results = response.json().get("shopping_results", [])
        prices = []
        for r in results:
            if r.get("price"):
                # Split the price string and convert each part to float
                price = r.get("price")
                price_parts = price.replace('$', '').replace(',', '').split()
                for part in price_parts:
                    try:
                        prices.append(float(part))
                    except ValueError:
                        pass # Ignore parts that can't be converted to float

        # Calculate the average price and update the device_info
        device_price = round(sum(prices) / len(prices), 2) if prices else None
        device_info[device] = {
            'timestamp': current_timestamp,
            'price': device_price
        }

        # Add the device price and timestamp to the user data
        user_tracked_data['device_price_USD'] = device_price
        user_tracked_data['price_updated_at'] = current_timestamp

    else:
        # Use cached price data if available and not outdated
        user_tracked_data['device_price_USD'] = device_data.get('price')
        user_tracked_data['price_updated_at'] = device_data.get('timestamp')

    return user_tracked_data

3. Create your pipeline#

In creating the pipeline, the fetch_average_price can be used in the following ways:
- Add map function
- Transformer function
The dlt library's transformer and add_map functions serve distinct purposes in data
processing.

Transformers are used to process a resource and are ideal for post-load data transformations in a
pipeline, compatible with tools like dbt, the dlt SQL client, or Pandas for intricate data
manipulation. To read more:
Click here.

Conversely, add_map is used to customize a resource and applies transformations at an item level
within a resource. It's useful for tasks like anonymizing individual data records. More on this
can be found under
Customize resources in the
documentation.

Here, we create the pipeline and use the add_map functionality:

# Create the pipeline
pipeline = dlt.pipeline(
    pipeline_name="data_enrichment_one",
    destination="duckdb",
    dataset_name="user_device_enrichment",
)

# Run the pipeline with the transformed source
load_info = pipeline.run(tracked_data.add_map(fetch_average_price))

print(load_info)

Info

Please note that the same outcome can be achieved by using the transformer function. To
do so, you need to add the transformer decorator at the top of the fetch_average_price function.
For pipeline.run, you can use the following code:

# using fetch_average_price as a transformer function
load_info = pipeline.run(
    tracked_data | fetch_average_price,
    table_name="tracked_data"
)

This will execute the fetch_average_price function with the tracked data and return the average
price.

Run the pipeline#

Install necessary dependencies for the preferred
destination, for example, duckdb:
```
pip install "dlt[duckdb]"
```
Run the pipeline with the following command:
```
python device_enrichment_pipeline.py
```
To ensure that everything loads as expected, use the command:
```
dlt pipeline <pipeline_name> show
```
For example, the "pipeline_name" for the above pipeline example is data_enrichment_one; you can use
any custom name instead.