Tracking Events for Decisive Completion with Kafka Streams Interactive Query V2

Do you have to make analytical decisions like closure of business tasks or concluding the day’s processing based on events?

Does tracking events based on specific business keys using - Aggregation, group-by, and repartition skewing records?

Read-write separation of a running stream is a solution using Kafka Streams Interactive Queries. Kafka Streams can expose the application state, It provides access to Materialized state stores or custom-created state stores (local to partition or global) for making iterative, range-based queries.

This session compares different ways of implementing event tracking in Kafka Streams:

Using Kafka Streams DSL functions to track count Using the global state store for keeping track of events Using Read-Write Separation approach with Interactive Queries V2 (IQ)

Learn how to use Interactive Query V2 to fetch running counters per instance and consolidate them. See how staging or scheduling count collection using IQ can lead to windowed count collection (effectively running on a wall clock) for a business key, improving overall performance and providing a consolidated view of data.