This post was co-written with Damian Guy, Engineer at Confluent, Michael Noll, Product Manager at Confluent and Neha Narkhede, CTO and Co-Founder at Confluent. We are excited to announce Interactive Queries, a new feature for stream processing with Apache Kafka®. Interactive Queries allows you to get more than just processing from streaming. It allows you to treat the stream processing layer as a
In this post I’m going to help you understand how Kafka stores its data. I’ve found understanding this useful when tuning Kafka’s performance and for context on what each broker configuration actually does. I was inspired by Kafka’s simplicity and used what I learned to start implementing Kafka in Golang. So how does Kafka’s storage internals work? Kafka’s storage unit is a partitionA partition is
2. Technical Presentation ● Familiarity with akka-streams flow and graph DSL’s ● Familiarity with big data and real time streaming platforms ● Familiarity with scala ● Effort between the akka-streams and Gearpump teams started late last year ● Resulted in a number of pull requests into akka-streams to enable different materializers ● Close to completion with good support of the akka-streams DSL (a
Flink How To: A Demo of Apache Flink with Docker September 29, 2016. Written by Gezim Sejdiu. Posted in Blog, Big Data, BDE-Technology This guide explains the steps of how to run a Flink application on the BDE platform. Apache Flink is an open-source platform for distributed stream and batch processing. In this post, we are going to see how to launch a Flink demo app in minutes, thanks to the Apac
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