Apache Kafka continues its ascent as attention shifts from lumbering Hadoop and data lakes to real-time streams ...

Data stream processing is defined as a system performing transformations for creating analytics on data inside a stream. In Part 1 of this series, we defined data streaming to provide an understanding ...

In this first installment of a three-part series on data streaming, Apache Kafka, and data analytics, we will dive into fully explaining data transaction streaming and the technologies that make it ...

Confluent Inc. today announced new features in its cloud service that make it easier for users of its Apache Kafka-based streaming engine to store data in the Apache Iceberg table format. The new ...

Unlike traditional enterprise messaging software, Kafka is able to handle all the data flowing through a company, and do it in near real time. This is desperately needed as data volumes skyrocket.

The insurance group is using Kafka’s data streaming capabilities to integrate disparate data sources and provide real-time data services to support its business.

In this article, author Robin Moffatt shows how to use Apache Kafka and KSQL to build data integration and processing applications with the help of an e-commerce sample application.

A data streaming solution must account for speed, security, scaling and more if it’s to provide the productivity, service and information it’s designed to deliver.

When Confluent launched a cloud service in 2017, it was trying to reduce some of the complexity related to running a Kafka streaming data application.