Retail — Retail E-Commerce Sales Feature Engineering Pipeline

Overview

The purpose of this DAG is to create valuable features from sales and inventory data that will be utilized in predictive maintenance models for retail e-commerce. The data sources include historical sales records, inventory levels, and promotional activities, which are ingested into the system for processing. The ingestion pipeline begins with data extraction from various sources, followed by a series of transformation steps that include normalization, aggregation, and enrichment to ensure that

The purpose of this DAG is to create valuable features from sales and inventory data that will be utilized in predictive maintenance models for retail e-commerce. The data sources include historical sales records, inventory levels, and promotional activities, which are ingested into the system for processing. The ingestion pipeline begins with data extraction from various sources, followed by a series of transformation steps that include normalization, aggregation, and enrichment to ensure that the features are relevant and actionable. Quality controls are implemented to monitor the integrity of the data throughout the processing stages. The outputs of this DAG are stored in a data warehouse, making them readily accessible for model training and analysis. Key performance indicators (KPIs) tracked during this process include processing time and the quality of the generated features, which are essential for assessing the effectiveness of the feature engineering process. In the event of a failure, the DAG is designed to restart automatically after an alert, ensuring minimal disruption to the workflow. This robust feature engineering pipeline ultimately enhances the predictive capabilities of sales forecasts, leading to improved inventory management and optimized sales strategies in the retail sector.

Part of the Fraud & Anomaly Analytics solution for the Retail industry.

Use cases

• Improved accuracy of sales forecasts
• Enhanced inventory management through predictive insights
• Increased operational efficiency via automated processes
• Better decision-making with data-driven insights
• Reduced risk of stockouts and overstock situations