Apache Spark


25

May 2017

IOT data analysis at scale with Tableau and SNAP

SNAP, is being used by ad-tech, fortune 50 and in general, companies that have large amounts of data. An area that SNAP is particularly good at is analyzing multi-dimensional data for very fast ad-hoc queries. Structured IOT data and machine data from sensors in devices are particularly suited for this. For example take a sample dataset recording readings from sensors every minute about temperature and...

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10

May 2017

Tableau and OLAP analytics on Hadoop data

Most customers find Hadoop based query access to tools like Tableau cumbersome and slow. When you account for concurrency requirements of enterprises, using a B.I tool on top of Hadoop turns to a project with a slew of summarized extracts consuming enormous resources and time and leading to productivity sinks. With SNAP we have a different approach to dealing with the ad-hoc query requirements on...

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28

Apr 2017

Sensors, IOT data and Spark

When we think of Big data we think of media, ad tech and the social apps generating billions of events. Other industries such as energy and medical device industry have always produced data – generally large amounts of it. The explosion in data for these industries is happening with “sensors”. Connected computing devices with sensors collect and transmit data. This data has to be harnessed,...

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05

Apr 2017

Querying S3 datalakes – SQL and Tableau on S3

For many, AWS S3 is not just a deep storage, but a viable option for storing data that can be consumed by reporting and analytics tools. Sparkline SNAP works seamlessly with S3 data directly and exposes your data to tools like Tableau with very fast response times across hundreds of concurrent user sessions. Below is a video of data from a Star Schema Benchmark data...

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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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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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