Data Flow Diagram for Processing System

This diagram illustrates a robust data processing architecture, adept at managing both live streams and historical datasets. Incoming data arrives through an 'Input' Kafka topic. From there, it branches into two main processing paths: a 'Speed Layer' for immediate insights and a 'Batch Layer' for comprehensive historical analysis. Both layers utilize Spark Streaming, indicating their capability to handle data in micro-batches or continuous streams. The 'Batch Layer' also integrates with HDFS, suggesting large-scale, durable data storage. The processed data is then made available through a 'Serving Layer,' represented by several instances, ensuring efficient data retrieval for applications. Furthermore, the system is designed to consume 'Models + Updates' from a dedicated Kafka topic, allowing for dynamic adjustments and continuous learning within the processing pipeline.

data pipeline - spark streaming - kafka - hdfs - data processing - big data - system architecture