Blog


14

Nov 2017

Exploring Big Data with Sparkline SNAP, Spark SQL and nteract with Python

Sparkline SNAP is used as a full fledged datawarehouse in place of traditional MPPs at large enterprises where fast data access is required for ad hoc analysis and reporting. Increasingly we see data engineers, used to tools like Jupyter notebooks, accessing SNAP data because its fast and iterative and simple to use with Python. Below is an example of a typical B.I drill down analysis...

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28

Oct 2017

Enterprise B.I platform at scale

More and more of Enterprise Data is moving to Data Lakes: which could be on an on-premise scale out cluster, but increasingly it could just as well be a cloud object store.  Enterprises are in the process of leveraging these datasets for a variety of analysis: from Operational Analytics to Reporting to Business Intelligence/Data Science and everything in between.  There is a plethora of  scale-out...

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24

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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27

Sep 2017

5 ways to rethink your data access strategy

Leveraging data is increasingly becoming critical to business success. However much of the data is locked in slow datamarts, legacy OLAP cubes and inaccessible Hadoop clusters. As a result, business users are stuck in second gear on an old car, unable to move at the speed needed to execute effectively. Dashboards, A.I and cool visualizations may dominate the conversation around analytics and B.I, but the...

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06

Sep 2017

ROI with SNAP

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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