Blog


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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07

Feb 2017

Advanced Tableau on Spark /Hadoop

Most benchmarks on datawarehouse optimizations and SQL engines stop with simple examples. The real world uses business intelligence tools where the use cases are not single user single SQL as in a simulated benchmark, Modern B.I on Big Data should satisfy three key requirements Should be able respond interactively as a user drills down into data in Hadoop/Spark, in seconds. While B.I is not about retrieving...

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04

Nov 2016

Optimizing an Enterprise Datawarehouse on Hadoop

As companies move from analytic datamarts and datawarehouses built on Teradata, Vertica or even Oracle/MYSQL to a Hadoop based architecture, consumption of data for B.I and Analytics workloads become critical. Hadoop has traditionally not been geared for consumption of data as users of Tableau know very well. Hive queries are slow. Products like Impala and Presto have eased the pain a bit but the challenge...

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