Big Data & Streaming — Study Guide

A visual, exam-aligned study guide for the Big Data & Streaming certification. Five modules cover big-data foundations and distributed storage, Apache Kafka in depth, stream processing with event-time semantics and windowing, stateful processing with delivery guarantees and checkpointing, and streaming architectures with real-time analytics and operations — with Mermaid diagrams, comparison tables, worked examples, key takeaways and self-checks.

Key facts

• Course price: $195 (one-time payment)
• Field: Data Engineering & Analytics
• Prepares for: Big Data & Streaming
• 5 modules · 15 lessons · about 4 hours of lessons
• Self-paced, online, illustrated lessons

Course outline

1. Big Data Foundations — The 5 Vs, distributed storage (HDFS and object storage) and the move-compute-to-data principle.
   • The 5 Vs of Big Data
   • Distributed Storage: HDFS to Object Storage
   • Distributed Computing Fundamentals
2. Apache Kafka In Depth — Topics, partitions, offsets, consumer groups, replication, retention and log compaction.
   • Topics, Partitions & Offsets
   • Consumer Groups & Replication
   • Retention & Log Compaction
3. Stream Processing & Time — Spark vs Flink, event time vs processing time, windowing and watermarks for late data.
   • Engines & Event Time
   • Windowing
   • Watermarks & Late Data
4. State, Guarantees & Fault Tolerance — Stateful processing, checkpointing/recovery and delivery semantics (at-most/at-least/exactly-once).
   • Stateful Processing & Checkpointing
   • Delivery Semantics
   • Exactly-Once End to End
5. Architectures, Analytics & Operations — Lambda vs kappa, CDC, backpressure, the streaming lakehouse, real-time OLAP and monitoring.
   • Lambda vs Kappa & CDC
   • The Streaming Lakehouse & Real-Time OLAP
   • Backpressure, Scaling & Monitoring