Announcing Confluent Cloud: Apache Kafka as a Service
Today, I’m really excited to announce Confluent CloudTM, Apache Kafka® as a Service: the simplest, fastest, most robust and cost effective way to run Apache Kafka in the public cloud.
Project Metamorphosis: Wir präsentieren die Event-Streaming-Plattform der nächsten Generation. Mehr erfahren
Today, I’m really excited to announce Confluent CloudTM, Apache Kafka® as a Service: the simplest, fastest, most robust and cost effective way to run Apache Kafka in the public cloud.
This year, we were pleased to host the inaugural Kafka Summit, the first global summit for the Apache Kafka community. Kafka Summit 2016 contributed valuable content to help Kafka users
In May, we introduced our first enterprise-focused feature, Confluent Control Center, with the 3.0 release of Confluent. Today, more than 35% of the Fortune 500 companies use Kafka for mission-critical
Event sourcing as an application architecture pattern is rising in popularity. Event sourcing involves modeling the state changes made by applications as an immutable sequence or “log” of events. Instead
A few months ago, we announced the release of open-source Confluent Platform 3.0 and Apache Kafka 0.10, marking the availability of Kafka Streams — the new stream processing engine of
This blog post is written jointly by Stephan Ewen, CTO of data Artisans, and Neha Narkhede, CTO of Confluent. Stephan Ewen is PMC member of Apache Flink and co-founder and CTO
I am very excited to announce the availability of the 0.10 release of Apache Kafka and the 3.0 release of the Confluent Platform. This release marks the availability of Kafka
We at Confluent are pleased to announce that the inaugural Kafka Summit, the first global summit of the Apache Kafka community, is happening today at the Hilton in San Francisco.
A few months ago, we announced the major release of Apache Kafka 0.9, which added several new features like Security, Kafka Connect, the new Java consumer and also critical bug
For a long time, a substantial portion of data processing that companies did ran as big batch jobs — CSV files dumped out of databases, log files collected at the
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