Ten things to consider for Interactive Analytics on high volume, write-once workloads
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.
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
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