Real-Time E-Commerce Data Pipeline Using Apache Kafka and Spark

To design and implement a real-time data engineering pipeline that collects, processes, and analyzes e-commerce transaction data using distributed streaming technologies. The system aims to enable real-time analytics, fraud detection, and business insights through scalable and fault-tolerant architecture.

Study the fundamentals of real-time data engineering and streaming architectures.

Install and configure Apache Kafka for real-time data ingestion.

Simulate e-commerce transaction data using Python scripts or APIs.

Create Kafka producers to publish transaction events to topics.

Develop Kafka consumers to subscribe and process data streams.

Integrate Apache Spark Streaming for real-time data transformation and aggregation.

Perform data cleaning, filtering, and enrichment operations.

Store processed data in a distributed storage system like HDFS or cloud storage.

Load aggregated insights into a relational database or NoSQL database.

Create dashboards using Power BI or Tableau for visualization.

Implement basic fraud detection rules based on transaction patterns.

Optimize the pipeline for fault tolerance and scalability.

Conduct performance testing and latency analysis.

Document system architecture with proper data flow diagrams.

Prepare final deployment and demonstration of the working pipeline.