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Event Stream Processing  Content on InfoQ rss

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Microservices and Stream Processing Architecture at Zalando Using Apache Flink by Srini Penchikala Posted on Oct 31, 2016 1

Stream Processing and Lambda Architecture Challenges by Alexandre Rodrigues Posted on Oct 19, 2016 4

Confluent Announces Kafka for the Enterprise with Multi-Datacenter Replication by Srini Penchikala Posted on Oct 05, 2016

Neha Narkhede: Large-Scale Stream Processing with Apache Kafka by Ralph Winzinger Posted on Jun 19, 2016

Comparison of Event Sourcing with Stream Processing by Jan Stenberg Posted on May 25, 2016

Azure Stream Analytics Publishing to Power BI Reaches General Availability by Kent Weare Posted on May 11, 2016

Apache Storm Reaches 1.0, Brings Improved Performance, Many New Features by Sergio De Simone Posted on Apr 14, 2016

Microservices for a Streaming World by Jan Stenberg Posted on Mar 14, 2016

Moving from Transactions to Streams to Gain Consistency by Jan Stenberg Posted on Mar 13, 2016

Netflix Details Evolution of Keystone Data Pipeline by Dylan Raithel Posted on Mar 04, 2016

Architecting Scalable, Dynamic Systems when Eventual Consistency Won’t Work by Michael Stiefel Posted on Jan 20, 2016

The Basics of Being Reactive by Jan Stenberg Posted on Jan 20, 2016

Yahoo! Benchmarks Apache Flink, Spark and Storm by Abel Avram Posted on Dec 23, 2015

CQRS, Read Models and Persistence by Jan Stenberg Posted on Oct 20, 2015 4

Introducing Reactive Streams by Jan Stenberg Posted on Sep 30, 2015

DDD, Events and Microservices by Jan Stenberg Posted on Jun 29, 2015 1

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InfoQ eMag: Hadoop Apache Hadoop is proving useful in deriving insights out of large amounts of data, and is seeing rapid improvements. Hadoop 2 now goes beyond Map-Reduce; it is more modular, pluggable and flexible and it fits a variety of use cases better. We explore this as well as some tools that can help utilize Hadoop better.
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