Big Data: Let's SMACK


October 10th, 2017 / 1:00 PM CEST


For many use cases such as fraud detection or reacting on sensor data the response times of traditional batch processing are simply to slow. In order to be able to react to such events close to real-time, we need to go beyond the classical batch processing and utilize stream processing systems such as Apache Spark Streaming, Apache Flink, or Apache Storm. But these systems are not sufficient by itself. One common example for such fast data pipelines is the SMACK stack using Apache Spark, Mesos, Kafka, Akka, Cassandra, Kafka.

Jörg Schad

Jörg Schad is a Developer Evangelist at Mesosphere who works on DC/OS and Apache Mesos. Prior to this he worked on SAP Hana and in the Information Systems Group at Saarland University. His passions are distributed (database) systems, data analytics, and distributed algorithms and his speaking experience include various Meetups, international conferences, and lecture halls.  

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