Taking KSQL for a Spin Using Real-time Device Data
We are pleased to invite Tom Underhill to join us as a guest blogger. Tom is Head of R&D at Rittman Mead, a data and analytics company who specialise in
Register for Apache Kafka®, Confluent, and the Data Mesh
We are pleased to invite Tom Underhill to join us as a guest blogger. Tom is Head of R&D at Rittman Mead, a data and analytics company who specialise in
It has been seven years since we first set out to create the distributed streaming platform we know now as Apache Kafka®. Born initially as a highly scalable messaging system,
After three successful Kafka Summits in the United States in 2016 and 2017, we decided the Kafka community was ready to have a Summit in London. And based on the
This guest blog post is the second in a series about the use of Apache Kafka’s Streams API by Zalando, Europe’s largest online fashion retailer. See Ranking Websites in Real-time
This article is by Hunter Kelly, Technical Architect at Zalando. Hunter enjoys using technology, and in particular machine learning, to solve difficult problems. He’s a graduate of the University of
Kafka Streams makes it easy to write scalable, fault-tolerant, and real-time production apps and microservices. This post builds upon a previous post that covered scalable machine learning with Apache Kafka,
KSQL is the streaming SQL engine for Apache Kafka®. It lets you do sophisticated stream processing on Kafka topics, easily, using a simple and interactive SQL interface. In this short
At Pinterest, we use Kafka Streams API to provide inflight spend data to thousands of ads servers in mere seconds. Our ads engineering team works hard to ensure we’re providing
Scalable Machine Learning in Production with Apache Kafka® Intelligent real time applications are a game changer in any industry. Machine learning and its sub-topic, deep learning, are gaining momentum because
Datacenter downtime and data loss can result in businesses losing a vast amount of revenue or entirely halting operations. To minimize the downtime and data loss resulting from a disaster,
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