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Recorded Webinar

Big Data and Fast Data: Using MapReduce in Hazelcast

59 minutes
Recorded May 21, 2014

In This Webinar:

Today’s amounts of collected data are showing a nearly exponential growth. More than 75% of all data has been collected in the past 5 years. To store this data and process it in an appropriate time you need to partition the data and parallelize the processing of reports and analytics.

This talk will demonstrate how to parallelize data processing using Hazelcast, MapReduce and it’s underlying distributed data structures. With an brief intro to the different terminology, followed by a live coding example, we will take a journey into distributed computing.

We’ll cover the following topics:

  • MapReduce Basics
  • Parallelizing an algorithm
  • API Overview
  • Coding Example
  • Q&A Session


Christoph Engelbert, Senior Solutions Architect at Hazelcast


Christoph Engelbert, Senior Solutions Architect at Hazelcast

In the early days Christoph started with Basic and C64 I later moved over C++ but eventually started Java around 2005. After some years of web development he started to get interested in the JVM internals and what makes a Java application fast and memory efficient. Now Christoph is mostly helping companies making efficient and scalable architectures and so he eventually landed at planet Hazelcast.

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