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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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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Sep 2016

Going beyond Data Lakes

We often see customers start to build data lakes on Hadoop or S3 as way to get their transactional data with dimensional data in a common place. This data is cleaned and organized in a star schema like in an enterprise data warehouse. The challenge begins here since consuming data in a Hadoop data lake is not easy. The first challenge is ad hoc analytics....

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Jul 2016

Terabyte scale Data Lake analytics on S3, Hadoop with Spark

In our recent work with customers, there is one constant. The need to make sense of terabytes of fact and time-series data that lands in the datalake( Physically S3 or HDFS). Here is a typical process before we get engaged.  The first step in this process is organizing data in the datalake. A typical fact table for our customers, such as events of all advertising-exposures...

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Jun 2016

Fast B.I on Spark SQL

A typical slice and dice query on a database has the following pattern. On large datasets, the response for such interactive queries have to be in the order of 1 or 2 seconds as users navigate across different Tableau worksheets or choose filters on their web application. A standard in-memory solution may be suboptimal for such slice and dice queries. First, caching large amounts of data...

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