Complex event processing (CEP) is a highly effective and optimized mechanism that combines several sources of information and instantly determines and evaluates the relationships among events in real-time. It is […]

Over the past few years, Apache Kafka has emerged as the top event streaming platform for streaming data/event ingestion. However,  in earlier version 3.5 of Apache Kafka, Zookeeper was the […]

At first, data tiering was a tactic used by storage systems to reduce data storage costs. This involved grouping data that was not accessed as often into more affordable, if […]

With the use of telemetry, data can be remotely measured and transmitted from multiple sources to a single place for control, analysis, and monitoring. The process of gathering data from […]

Transferring real-time data processed within Apache Flink to Kafka and ultimately to Druid for analysis/decision-making. Businesses can react quickly and effectively to user behavior patterns by using real-time analytics. This […]

Over the past few years, Apache Kafka has emerged as the leading standard for streaming data. Fast-forward to the present day, Kafka has achieved ubiquity, being adopted by at least […]

When data is analyzed and processed in real-time, it can yield insights and actionable information either instantly or with very little delay from the time the data is collected. The […]

Apache Kafka stands as a robust distributed streaming platform. However, like any system, it is imperative to proficiently oversee and control latency for optimal performance. Kafka Consumer Lag refers to […]

In today’s data-driven world, the capability to transport and circulate large amounts of data, especially video files, in real-time is crucial for news media companies. For example, an incident occurred […]

The Apache Kafka, a distributed event streaming technology, can process trillions of events each day and eventually demonstrate its tremendous throughput and low latency. That’s building trust and over 80% […]