Evolution of Apache Spark Structured Streaming - From Then to Now
Data Engineering and Ops Intermediate
Evolution of Apache Spark Structured Streaming, from its early micro-batching model to its modern, unified API for batch and streaming workloads. Key advancements include event-time processing, fault tolerance, and performance optimizations, enabling scalable, real-time data analytics with improved latency and throughput.
This presentation focuses on the evolution of Apache Spark™ Structured Streaming and its key features:
- Unified API for batch and streaming workloads.
- Incremental processing (Performant and Cost Efficient)
- Stateful Processing (Fraud Detection, Anomaly, sessionization)
- Optimizations for lower latency and higher throughput.
Structured Streaming provides a scalable, flexible solution for real-time data processing, combining batch and streaming in one framework.