Blog


31

Mar 2017

Fast analytics on Spark – Really fast

Interactive ad-hoc analytics requires fast responses. Fast, in many benchmarks are single user tests that do not really reflect the realities of how business users use an analytics or Big Data platform. A good test is a comprehensive simulation of Tableau users pounding a system with a variety of queries. We simulated one such use case on a single r3.2x node on AWS ( 6...

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31

Mar 2017

Integrated Business Intelligence on Big Data Stacks.

Read the new post from our CTO on the changing landscape of B.I and analytics on Big Data stacks. Until recently Big Data stacks have primarily focused on SQL capabilities. Of late, support for Business Intelligence(BI) applications and workloads is coming into focus for both Big Data providers and consumers. BI is not just faster, better SQL: in its essence BI is about enabling the Business Analyst...

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14

Mar 2017

Making data useful and ubiquitous

Datawarehouses have evolved over the years. With Hadoop reaching a level of maturity and Spark as a powerful engine to power various workloads, we are now at a point to truly democratize consumption of data to power insights. Savvy data driven companies, combine the power of automated data analysis with human insights. In order to get everyone in an organization to leverage the data, data...

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08

Mar 2017

SNAP – SparklineData Nextgen Analytics Platform

Many of you who have followed us over the past year or two, know that we have been heads down on making life easier for those who struggle with the challenges of ad-hoc analysis on modern data lakes. We have seen the frustrations of Tableau users and in the words of one of those users, ” We have a Ferrari in Tableau but using it...

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14

Feb 2017

Fast aggregations/metrics on Spark with Tableau

Ad-hoc queries, with sub second response time, is critical for enterprises. Vast amounts of data exist in Hadoop or AWS datalakes and consumption of this data, in a scalable /fast manner using existing B.I tools like Tableau, is a challenge.  Transactions at the lowest grain(hourly/daily etc), are stored in fact tables. In order to achieve an acceptable level of performance, companies resort to writing extracts or summary tables...

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