Confluent Blog

Back in May 2017, we laid out why we believe that Kafka Streams is better off without a concept of watermarks or triggers, and instead opts for a continuous refinement

During a recent talk titled Hunters ATT&CKing with the Right Data, which I presented with my brother Jose Luis Rodriguez at ATT&CKcon, we talked about the importance of documenting and

Building a scalable, reliable and performant machine learning (ML) infrastructure is not easy. It takes much more effort than just building an analytic model with Python and your favorite machine

Here at Zenreach, we create products to enable brick-and-mortar merchants to better understand, engage and serve their customers. Many of these products rely on our capability to quickly and reliably

Part 1 of this blog series introduced a self-paced tutorial for developers who are just getting started with stream processing. The hands-on tutorial introduced the basics of the Kafka Streams

Stream processing is a data processing technology used to collect, store, and manage continuous streams of data as it’s produced or received. Stream processing (also known as event streaming or

The phrase time value of data has been used to demonstrate that the value of captured data diminishes by time. This means that the sooner the data is captured, analyzed

The principle of least privilege dictates that each user and application will have the minimal privileges required to do their job. When applied to Apache Kafka® and its Streams API,

Machine learning and the Apache Kafka® ecosystem are a great combination for training and deploying analytic models at scale. I had previously discussed potential use cases and architectures for machine

Fraud detection is a topic applicable to many industries, including banking and financial sectors, insurance, government agencies and law enforcement and more. Fraud attempts have seen a drastic increase in