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4 Key Components to Adopt #BigData As Service Concept for Enterprise #Analytics

As organizations work to make big data broadly available in the form of easily consumable analytics, they should consider outsourcing functions to the cloud. By opting for a Big Data as a Service solution that handles the resource-intensive and time-intensive operational aspects of big data technologies such as Hadoop, Spark, Hive and more, enterprises can focus on the benefits of big data and less on the grunt work.

The 4 key components for this concept are: 1. High-performance, analytic-ready data store on Hadoop; 2. Semantic layer that facilitates “business language” data analysis; 3. A multi-tenant big data environment; and 4. User-friendly ways of consuming analytics.

Want to find out the details of Big Data as a Service concept? Raymie Stata has written an excellent article called Big Data as a Service delivers the analytics benefits without the grunt work on NetworkWork.

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