ROI on Data lake


Nov 2017

Multi-Dimensional analytics at scale

In a typical enterprise there are broadly two kinds of B.I projects . Focus on factual reporting and analysis – These projects involve implementing Hadoop or some Big Data stack for organizing and managing an enterprise datawarehouse and running SQL queries for reporting or connecting Tableau etc for slice and dice analysis – Some level of ad hoc querying using tools provided by single node B.I...

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Oct 2017

Apache Spark for enterprise datawarehousing

Most people, when they think of Apache Spark think machine learning and data science. Spark is so much more than that. Enterprises today, struggle to make sense of the alphabet soup in Hadoop. Big Data was synonymous with Hadoop. However Hadoop is not one thing. The biggest value of Hadoop for analytics and B.I was, and is HDFS which is a distributed file system. But...

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Sep 2017


We have seen before, from our benchmarking exercises, how efficient SNAP can be in providing the best performance at the lowest cost. SNAP does not need specialized hardware, GPUs or large clusters. Many benchmarks do not focus on real use cases involving true adhoc queries accessing multiple regions of a multi-dimensional  large dataset. For example running Tableau workloads is different from hand written benchmark queries....

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