Updated: July 15, 2020 (January 9, 2017)
Analyst ReportData Management and Storage
Data management and storage are the foundations for successfully processing Big Data workloads and together are likely the most expensive part of any Big Data solution. Data management requires a database engine that is able to receive and store data in a storage environment in such a way that it can be successfully and efficiently queried. Choosing the right data management offering depends on understanding the volume of data, the services used to collect the data, and the tools selected for analysis and reporting on the data so that it can deliver the appropriate level of performance. With some Big Data workloads, it can be important to collocate data and associated services to reduce latency and improve performance.
On-Premises and Hybrid
On-premises, customers have full control over how and where data is stored, but they also have responsibility for supplying the necessary hardware and software, including the initial investment and maintenance.
Options include native Hadoop clusters, SQL Server 2016 and PolyBase, and Analytics Platform System (APS).
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