Data Processing with Structured Streaming focuses on handling real-time data streams using scalable and fault-tolerant stream processing frameworks. Structured Streaming in Apache Spark processes live data as incremental batches while providing high-level APIs for continuous analytics. This training explains how streaming pipelines ingest, transform, and analyze data from sources such as Kafka, logs, and IoT devices. It also covers window operations, watermarking, stateful processing, and fault tolerance for reliable stream management. You will learn how to build scalable real-time data applications that support monitoring, analytics, and event-driven systems. The course also highlights best practices for optimizing performance and managing low-latency streaming workflows in distributed environments.
Showing the single result
