Balancing Performance, Scalability and Cost of Analytic Workloads on Elastic Clouds


By Rohit Tandon
Sizing up any processing workload, whether Data Warehousing (aggregations, mostly I/O intensive) or any Data Sciences processing workload (primarily CPU intensive, but depends on algorithms) is a matter of diligent analysis that relies on multiple factors. For example: Is the processing or algorithm CPU intensive or I/O intensive Data volumes Aggregations Indexing Code quality & efficiency Programming language / Tool of choice Data store being used and where it...

Data Cloud Pilots


By Vin Siegfried
The current cycle of innovation and especially data innovation has been romanticized and lampooned, in television series (Silicon Valley) in business books and in movies.  The terms used are now familiar to most of us:  “MVP” is not only the best player on the ball team.  “Early Exit” is not only when you leave a boring party early.  Rounds A, B, C are not only stages in a darts tournament.  There are good reasons this terminology is slipping into our...

Metadata and Data Lakes: Retaining Corporate Memory


By Vin Siegfried
We are in a very special period in the history of technology, where new and real innovation in data management is in the news every day.  It all has me worried however.  It reminds me of   “Flowers for Algernon,” an award-winning short story which was made into a good movie called Charly.  The story line is poignant—a mentally disabled man is given an experimental surgery which at first improves his cognitive capabilities modestly but then continues to...

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