What you'll learn

Streaming systems must handle high-velocity data, maintain accurate states, and continue operating reliably even when failures occur. Without proper state management and fault-tolerance mechanisms, real-time analytics, alerts, and business metrics can become inconsistent or lost.

In this course, Understanding State and Fault Tolerance in Stream Processing, you’ll gain the ability to design and implement reliable, scalable, and fault-tolerant stream processing applications.

First, you’ll explore the concept of state in streaming applications, learning how to use counters, aggregations, joins, and windows to manage information efficiently.

Next, you’ll discover fault-tolerance mechanisms, including checkpointing, snapshots, state persistence, and recovery strategies to ensure systems continue running correctly after failures.

Finally, you’ll learn how to choose and implement the right delivery guarantees, such as at-most-once, at-least-once, and exactly-once semantics, and how to handle retries, duplicates, and idempotent operations in real-world streaming pipelines.

When you’re finished with this course, you’ll have the skills and knowledge of state management, fault-tolerant design, and delivery guarantees in stream processing needed to build reliable and accurate real-time data applications.