Here are three trends I’ve been seeing lately around the Kafka, Flink, and Iceberg communities. Each presents a new way for engineers to manage data and meet application needs. Re-envisioning ...

Kafka powers these transformations by putting data at the heart of the business and unifying everything that happens into streams of real-time events.” About the 2018 Apache Kafka Report ...

It’s become almost a standard career path in Silicon Valley: A talented engineer creates a valuable open source software commodity inside of a larger organization, then leaves that company to create a ...

These Kafka-based systems support a variety of downstream applications, like data warehousing, application and system monitoring, recommendation engines, and security and fraud detection (see fig 1).

That allowed customers to blend enterprise applications with the cloud’s scale-out, distributed computing and microservices. Kafka uses a “publish-and-subscribe” model that links data ...

Originally developed at LinkedIn, Kafka is an open-source system for managing real-time streams of data from websites, applications and sensors. Essentially, it acts as a sort of enterprise ...

Kafka acts as a central data backbone and enables loose coupling between applications (Akka), data processing (Spark) and data persistence (Cassandra) services. The problem of managing data in ...

In addition, Uravel says, it can troubleshoot and tune applications that are unable to keep up with input data rates in Kafka. Unravel also remediates load imbalances in Kafka caused by factors such ...

Automatically detect and alert on atypical Kafka behavior to prevent data loss Ensures preservation of SLAs for real-time stream processing applications Forecast Kafka data streaming capacity needed ...

“Our new Kafka integration gives data teams confidence in the reliability of the real-time data streams powering these critical services and applications, from event processing to messagin ...