The multiple V’s of Big Data require the hardware architecture operating it to be “distributed” and rooted in parallel processing for the most efficient management possible. Although the data sources for Big Data can be anywhere, when the multi-channel data is pulled into the Big Data environment, then the two significant characteristics that distinguish Big Data from other types of data is scale and processing power.Big data can be described in terms of data management challenges that – due to increasing volume, velocity and variety of data – cannot be solved with traditional databases.

Big Data as a Service (BDaaS) is the new mantra for Enterprise Data Management. Till recently, the term BDaaS was used to imply a broad range of data services hosted on Cloud platforms. In a typical bundled solution, other related services like Software as a Service (SaaS) or Infrastructure as a Service (IaaS) were used to deliver Big Data enabled Data Management solutions. The biggest drawback, in spite of many business benefits, of such a solution has been the lack of user control over the data path.