Kafka performance relies on implementing continuous intelligence and real-time analytics. It is important to be able to ingest, check the data, and make timely business decisions.
Stream processing systems provide a unified, high-performance architecture. This architecture processes real-time data feeds and guarantees system health. But, performance and reliability are challenging. IT managers, system architects, and data engineers must address challenges with Kafka capacity planning to ensure the successful deployment, adoption, and performance of a real-time streaming platform. When something breaks, it can be difficult to restore service, or even know where to begin.
This webinar discusses best practices to overcome critical performance challenges for Kafka data streaming that can negatively impact the usability, operation, and maintenance of the platform, as well as the data and devices connected to it. Topics include: Kafka data streaming architecture, key monitoring metrics, offline partitioning, broker, topics, consumer groups, and topic lag.