Systems | Development | Analytics | API | Testing

Transitioning to a set of microservices can seem daunting. This video breaks down how the Designing Event-Driven Microservices course can help you navigate that journey.

This video covers setting an idle timeout on a watermark generator when joining data in Apache Flink. This can be used when you have two streams, one that has frequent updates, and one that has infrequent updates, and you need to join data without waiting for a fresh watermark from the infrequent one.

Apache Kafka 3.8 is here! On behalf of the Kafka community, Danica Fine highlights key release updates, with KIPs from Kafka Core, Kafka Streams, and Kafka Connect. Kafka Core: Kafka Streams: Kafka Connect.

In this video, we'll look at some techniques for evolving events by analyzing a specific use case in a banking fraud detection system.

When developing or debugging a stream processing pipeline with Flink SQL, it’s common to inspect each processing step's output to ensure data is being transformed properly. However, comprehending the resulting data stream's structure, distribution, and characteristics entails executing multiple ad-hoc SQL queries, which can be time-consuming and tedious. Additionally, isolating specific subsets of the stream for analysis or debugging often involves even more queries, adding to the complexity and time required.

How can engineers enable real-time insights when working with high-throughput, data-intensive streams? In this 30-minute session, Imply and Lenses.io show you how to Enable self-service access for developers working with critical, high-velocity data flows in #apache #kafka Ingest and normalize complex data structures, enabling real-time analytics at scale via modern databases like #druid.

Learn how to leverage the native monitoring capabilities of the Python Kafka producer along with Confluent Cloud’s Metrics API while exploring how linger.ms affects latency and batch sizes.