The premier international event designed for and by performance and capacity professionals
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Boris Zibitsker, CEO of BEZNext
Today’s fast-paced businesses have to make business decisions in real time. That creates pressure on IT leaders to develop Big Data infrastructure and applications capable to process large volume of data from different sources, apply advanced analytics and present recommendations in real time.
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Larry Strickland, Chief Products Officer, DataKinetics
As Big Data repositories are becoming more strategic to an enterprise, the need to populate them from legacy mainframe databases is percolating to the top of the priority list. Moving business-critical mainframe data into Hadoop, NoSQL or other Big Data databases presents a number of challenges vs populating a relational based data warehouse.
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Praveen Rao Gennepally, Performance Engineering CDG & Tech Mahindra
The sheer volume of data increases the complexity of big data applications and performance evaluation cannot be done in the traditional way resulting in delayed implementation, poor user experience in production and increase in cost of big data application performance assurance
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Odysseas Pentakalos, CTO, SYSNET International
In this tutorial we will first introduce the audience to Hadoop and the MapReduce framework and how they can be utilized to process large amounts of data. We will then discuss the Spark distributed data processing engine, stream processing systems such as Kafka and Storm. This is a 3 part tutorial.
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