Retail — Retail E-Commerce Data Ingestion Pipeline

Overview

The primary purpose of this DAG is to automate the ingestion of critical sales and inventory data from ERP and CRM systems, as well as IoT logs from point-of-sale devices. The architecture includes multiple connectors that facilitate the extraction of data from these diverse sources, ensuring a seamless flow into the data warehouse. The ingestion pipeline begins with data extraction, followed by normalization to ensure consistency across different formats. Quality control measures are implemente

The primary purpose of this DAG is to automate the ingestion of critical sales and inventory data from ERP and CRM systems, as well as IoT logs from point-of-sale devices. The architecture includes multiple connectors that facilitate the extraction of data from these diverse sources, ensuring a seamless flow into the data warehouse. The ingestion pipeline begins with data extraction, followed by normalization to ensure consistency across different formats. Quality control measures are implemented at various stages to validate data integrity, including checks for completeness and accuracy. The outputs of this process are structured datasets that can be utilized for advanced analytics and predictive maintenance strategies. Key performance indicators (KPIs) for monitoring the pipeline's effectiveness include ingestion time and error rates, providing valuable insights into operational efficiency. Additionally, the DAG is designed with an automatic restart feature in case of failures, triggered by alerts, ensuring minimal disruption to data availability. By leveraging this automated data ingestion process, retail organizations can enhance their decision-making capabilities, optimize inventory management, and improve overall customer experience through timely insights.

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

Use cases

• Improved data accuracy and reliability for decision making
• Faster access to critical sales and inventory information
• Enhanced predictive maintenance capabilities for retail operations
• Reduced manual effort in data handling and processing
• Increased operational efficiency through automated workflows