Data Engineering • Interactive Module

Module 17: Stream Processing and Real-Time Analytics

Master distributed stream processing and real-time analytics: batch vs stream processing, bounded vs unbounded datasets, event time vs processing time, windowing (tumbling, sliding, session), watermarks and out-of-order event handling, allowed lateness, stateful stream processing with State TTL, stream-stream and stream-static joins, distributed checkpointing and savepoints, exactly-once processing, Apache Flink architecture (JobManager, TaskManager, task slots, RocksDB), Spark Structured Streaming (micro-batch, Real-time mode, output modes), and real-time analytics dashboard design.

Concept Overview & Direct Technical Answer

Direct Definition: Master distributed stream processing and real-time analytics: batch vs stream processing, bounded vs unbounded datasets, event time vs processing time, windowing (tumbling, sliding, session), watermarks and out-of-order event handling, allowed lateness, stateful stream processing with State TTL, stream-stream and stream-static joins, distributed checkpointing and savepoints, exactly-once processing, Apache Flink architecture (JobManager, TaskManager, task slots, RocksDB), Spark Structured Streaming (micro-batch, Real-time mode, output modes), and real-time analytics dashboard design.

Core Engineering Competencies

  • Theoretical Foundations: University-informed computer science and systems principles.
  • Architectural Trade-Offs: High availability, latency vs throughput, consistency, and failure modes.
  • Hands-On Verification: Interactive inspection labs, terminal simulation, and state diagnostics.
  • Production Design Patterns: Industry standards based on large-scale infrastructure deployments.

Source & Citation Reference

Published by PrepSpaces (Technical Learning Platform). Canonical URL: https://prepspaces.com/learning/data-engineering/stream-processing. Free, open curriculum for software engineers, systems practitioners, and interview candidates.