Building a Retail Media Analytics Pipeline with PySpark: Bronze, Silver, and Gold
A retail-media dashboard can look straightforward: impressions, clicks, spend, conversions, and revenue by campaign. But the engineering underneath becomes more interesting when those metrics originate from several advertising platforms. To explore that problem, I put together a public portfolio project using PySpark, SQL, and the medallion architecture . It works with simulated campaign data…
Creating a retail media analytics pipeline using PySpark involves several stages, each building upon the previous one to transform raw data into actionable Key Performance Indicators (KPIs). This process begins with a Bronze layer that captures raw data, followed by a Silver layer that standardizes and cleans the data, and concludes with a Gold layer that aggregates data into meaningful KPIs.
The Bronze layer is essentially a raw storage for the data as it initially arrives. In a production environment, this layer would retain the original source records along with ingestion timestamps, batch identifiers, and source metadata, ensuring an audit trail should the platform’s schema change. This initial layer is where the data resides in its original format, ready for further processing.
Moving to the Silver layer, the data undergoes a transformation phase where data types are standardized and identifiers are normalized using PySpark. This involves parsing the event_date into a date format, lowercasing and trimming platform identifiers, trimming campaign identifiers and names, casting count fields to long and monetary measures to double, removing duplicates by date, platform, and campaign ID, and filtering out records that fail business rules. This step ensures the data is in a consistent and usable format for further analysis.
Finally, the Gold layer aggregates the cleaned data to produce KPIs such as Click-Through Rate (CTR), Cost Per Click (CPC), Cost Per Thousand Impressions (CPM), Conversion Rate, Cost Per Acquisition (CPA), and Return on Ad Spend (ROAS). These KPIs are calculated after aggregation rather than averaging each input row’s rate, ensuring that the results are accurate and meaningful.
The code uses safe division functions to handle division by zero scenarios and defines specific metrics for evaluation, such as ensuring that clicks do not exceed impressions.
Throughout the pipeline, data quality checks are crucial to ensure the reliability of the analytics. This involves checking for missing required columns, invalid business keys, null or negative metric values, and discrepancies such as clicks exceeding impressions. Instead of silently removing erroneous data, the pipeline includes a quality report module that aggregates counts of rule violations.
If any violations are found, the pipeline raises a ValueError, alerting analysts to potential data issues before further analysis or dashboarding.
In summary, building a retail media analytics pipeline with PySpark involves a systematic approach that starts with raw data preservation in the Bronze layer, progresses through data standardization and quality checks in the Silver layer, and culminates in the KPI aggregation in the Gold layer. Each layer builds upon the previous one, ensuring that the final analytics are accurate, reliable, and ready for use in decision-making processes.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
