Data Pipeline Orchestration for E-Commerce Analytics with Apache Airflow

In this project, Apache Airflow was used to build and orchestrate a data pipeline for an e-commerce company. The pipeline automates the ingestion, processing, and analysis of large datasets, covering transactions, customer profiles, and web behavior. This enables the company to gain real-time insights and make data-driven decisions on customer engagement and marketing strategies.

Project Objective

Automate the end-to-end data pipeline to support daily data updates. Process large amounts of data from multiple sources, including MySQL, Amazon S3, and Google Analytics. Provide a centralized framework to schedule and monitor ETL tasks, ensuring data availability for analysis. Generate daily and weekly reports for product performance, customer behavior, and sales trends.

Architecture and Workflow Design

  • Data Ingestion: Extract data from various sources: MySQL database for transactions, Amazon S3 for product details, and Google Analytics for customer interaction data. Data is ingested incrementally to handle large datasets efficiently. systems.
  • Data Processing and Transformation: Transform and clean raw data using Python and Apache Spark to handle any inconsistencies. Normalize data formats, manage missing values, and optimize for analysis.
  • Data Aggregation and Storage: Aggregate data based on daily transactions, top-selling products, and customer demographics. Store processed data in a centralized data warehouse (e.g., Amazon Redshift) for efficient querying.
  • Reporting and Analytics: Generate daily, weekly, and monthly reports for sales performance, customer segments, and marketing campaign effectiveness. Load aggregated results to Google Sheets and Slack for team access.
  • Monitoring and Alerts: Set up email alerts for pipeline failures, data inconsistencies, or missing data. Enable Slack notifications for monitoring task completion and failures.

Results

  • Improved Efficiency: Automated data pipeline reduced manual effort and minimized errors.
  • Real-Time Insights: Daily data refresh allowed stakeholders to view up-to-date analytics.
  • Enhanced Decision-Making: Aggregated reports provided insights on sales performance and customer engagement, driving more effective marketing strategies.