Big Data, Simple and Fast: Addressing the Shortcomings of Hadoop
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Recorded Webinar

Big Data, Simple and Fast: Addressing the Shortcomings of Hadoop

60 minutes
Recorded October 21, 2014

In this webinar

This talk identifies several shortcomings of Apache Hadoop and presents an alternative approach for building simple and flexible Big Data software stacks quickly, based on next generation computing paradigms, such as in-memory data/compute grids. The focus of the talk is on software architectures, but several code examples using Hazelcast will be provided to illustrate the concepts discussed.

We’ll cover these topics:

  • Briefly explain why Hadoop is not a universal, or inexpensive, Big Data solution – despite the hype
  • Lay out technical requirements for a flexible Big/Fast Data processing stack
  • Present solutions thought to be alternatives to Hadoop
  • Argue why In-Memory Data/Compute Grids are so attractive in creating future-proof Big/Fast Data applications
  • Discuss how well Hazelcast meets the Big/Fast Data requirements vs Hadoop
  • Present several code examples using Java and Hazelcast to illustrate concepts discussed
  • Live Q&A Session

Presenter

Jacek Kruszelnicki, President at Numatica Corporation

Presenter

Jacek Kruszelnicki, President at Numatica Corporation

Jacek Kruszelnicki is President of Numatica Corporation, a consulting firm helping clients with software development strategies, design and delivery of low-latency, high-throughput solutions, including Big/Fast Data. He has over 20 years of experience in distributed enterprise software as Software Architect and CTO, recently focusing on in-memory compute/data-grids, Big/Fast Data and Predictive Analytics. Jacek is a published author, mentor and conference speaker.

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