To build a real-time data engineering pipeline that processes financial transactions using streaming frameworks to detect fraud patterns instantly. The system ensures low latency processing, scalability, and integration with analytics dashboards for monitoring suspicious activities.
Study streaming data architecture fundamentals.
Simulate high-volume transaction datasets.
Use Kafka for real-time data ingestion.
Implement Spark Structured Streaming for processing.
Develop rule-based fraud detection algorithms.
Apply window-based aggregation for anomaly detection.
Store flagged transactions in NoSQL database.
Implement alert system for suspicious activities.
Optimize pipeline latency and throughput.
Deploy system in cloud environment.
Monitor performance using metrics dashboards.
Conduct stress testing with peak load scenarios.
Secure transaction data using encryption.
Document pipeline design and testing results.
