The Fifth Elephant 2014

With the advance of No-SQL and big data, there has been an explosion of database technologies. Each of them are best suitable for certain kind of work loads. For applications such as log analysis, sensor data analytics, genome data analytics, what is the framework to evaluate the best suitable databases. This session explains core technologies which benefit write-once workload and mapping to various industry databases of hadoop and related technologies.

Outline

CONTEXT – Write once data load - Ex. Time-series data. Which Database?

SSD is Good

MPP is Good

Columnar is Good

Logical Partition is Good

Data Skew Partition is Good

Search Engine Index could lead to Index Explosion

Concurrent Users First, Single Query Performance Next

High Throughput File level Snapshot Loading

Calculate cost upfront

Data Structure makes a Big Difference

DEMO

Speaker bio

CTO and Co-Founder at Bizosys Technologies since 2009

Created HSearch – a Real-time, distributed search and analytics engine built on Hadoop platform

Passion on distributed systems and data structures

Speaker at Fifth Elephant 2013, Microsoft Teched 2012 (Hadoop on Azure), Yahoo Hadoop India Summit 2011

Developed partitioning, read optimized data structures modules for HSearch.

Worked with a range of search products including Lucene, Solr, Endeca and FAST

Abinash is an engineering graduate of NIT, Raurkela

Slides

http://www.slideshare.net/AbinashaKarana/ten-things-to-consider-for-interactive-analytics-on-write-once-workloads