Introducing Confluent Platform version 7.5, which offers a range of new features to enhance security, improve developer efficacy, and strengthen disaster recovery capabilities. Building on the innovative feature set delivered in previous releases, Confluent Platform 7.5 makes enhancements to three categories of features: The following explores each of these enhancements and dives deep into the major feature updates and benefits.

In Part One of our “Inside Flink” blog series, we explored the critical role of stream processing and why developers are increasingly choosing Apache Flink® over other frameworks. In this second installment, we'll showcase how innovative teams across every industry and size are putting stream processing into practice – from streaming data pipelines to train ML models or more timely analytics to fraud detection in finance and real-time inventory management in retail.

Since the introduction of stream processing, there have been three certainties in life: death, taxes, and out-of-order data. As a stream processing library built for Apache Kafka, Kafka Streams processes data in offset order. When out-of-order data is present, offset order differs from timestamp order and care must be taken to ensure that processing results respect timestamp order where appropriate.

There was a huge amount of buzz about Apache Flink® at this year’s Kafka Summit London. From an action-packed keynote to standing-room only breakout sessions, it's clear that the Apache Kafka® community is hungry to learn more about Flink and how the stream processing framework fits into the modern data streaming stack.

Since the inception of our cloud journey, we have extensively utilized Let's Encrypt because it has been very reliable, fully automated, open, and free. Today, we’re proud to become an official sponsor of Let’s Encrypt. In this blog post, we’re celebrating this event by explaining our journey with Let’s Encrypt, how we integrate with their service, and why we chose them.