Getting the Most Out of Your NoSQL DB
Best Practices for Optimizing Infrastructure Performance and Budget
Thursday, August 7, 2014
Presented by: Alex Bordei
Duration: Approximately 60 minutes.
Hosted By: Ben Lorica
When harnessed correctly, hardware can generate performance improvements in software of up to 60% in an existing setup, with zero or minimal investment.
In this webcast Alex Bordei will look at how Impala, Elasticsearch and Couchbase perform when scaled vertically and horizontally, over a number of different bare metal setups. He'll discuss testing that produced results that included: going from one hex-core CPU to two deca-core CPUs, from 32 to 192 GB of RAM, from local to distributed storage, and from 2 to 14 instances.
Tune in to see what setup provided the best performance/price for each application and learn how to get more performance right now, from NoSQL DB.
About Alex Bordei
Alex Bordei has been developing infrastructure products for over nine years. Before becoming Bigstep's Product Manager, he was one of the core developers for Hostway Corporation's provisioning platform. He then focused on defining and developing products for Hostway's EMEA market and was one of the pioneers of virtualization in the company. After successfully launching two public clouds based on VMware software, he created the first prototype of Bigstep's Full Metal Cloud in 2011. He now focuses on guaranteeing that the Full Metal Cloud is the highest performance cloud in the world, for big data applications. Twitter: @alexandrubordei
About Ben Lorica
Ben Lorica is the Chief Data Scientist and Director of Content Strategy for Data at O'Reilly Media, Inc.. He has applied Business Intelligence, Data Mining, Machine Learning and Statistical Analysis in a variety of settings including Direct Marketing, Consumer and Market Research, Targeted Advertising, Text Mining, and Financial Engineering. His background includes stints with an investment management company, internet startups, and financial services. He writes regularly about Big Data and Data Science on the O'Reilly Data blog.
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