Real-Time Data Capture and Streaming with Kafka and Spark EMR

The goal is to capture the following data streams:

Traffic Features:

Collect network traffic data on the cloud server and extract relevant traffic features. HTTP Packet Data:

Monitor and collect HTTP packet data across the server for processing and analysis. eBPF Kernel Data:

Use eBPF (Extended Berkeley Packet Filter) to capture user-mode instructions and system calls transmitted through the kernel, for in-depth system monitoring. Real-Time Data Flow:

Use Kafka to stream the captured data in real time. Data Processing with Spark EMR:

Stream the Kafka data into Spark EMR for further processing and analytics. Reactor Pattern for Dashboard Integration:

Integrate the processed data into a dashboard using Reactor Pattern, ensuring that the data flow is non-blocking and real-time.