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Polybase allows you to run queries on external data in Hadoop or Azure blob storage. SQL Server 2016 comes with several features and tools to support cross-platform analytics. For example, Microsoft Dynamics AX, a cloud-scale online ERP offering, gains real-time insights by using a non-clustered columnstore index on their transactional tables to reduce aggregation latency from hours to seconds. Royce Kallesen, senior director of science and research at PROS says, “Microsoft R’s parallelization and enhanced memory management on the server integrated with SQL Server provides dramatically faster results on a common platform with built-in security.”Įliminating the need to move data out of the database for analytics dramatically reduces the latency for insights. PROS Holdings uses SQL Server 2016’s superior performance and built-in R Service to deliver advanced analytics more than 100x faster than before, resulting in higher profits for their customers.
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It is a profound simplification in how mission critical intelligent applications can be built and managed in the enterprise.Ī good example of how our customers are benefiting from the new model comes from PROS Holdings, Inc., a revenue and profit realization company that helps B2B and B2C customers achieve their business goals through data science. Furthermore, the database can serve as a central server for the enterprise’s analytical models and multiple intelligent applications can leverage the same models. Updating machine learning models, deploying new models, and monitoring their performance can now be done in the database without recompiling and redeploying applications. As a result, analytical applications can now be far simpler and need only query the database for analytic results. It introduces a new paradigm where all joins, aggregations and machine learning are performed securely within the database itself without moving the data out, thereby enabling analytics on real-time transactions with great speed and parallelism. SQL Server 2016 simplifies analytics in the way databases simplified enterprise data management, by moving analytics close to where the data is managed instead of the other way around.
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And deep analytics on real-time transactions are next to impossible without a lot of heavy lifting. This approach incurs high latency because of data movement, doesn’t scale as data volumes grow and burdens the application tier with the task of managing and maintaining analytical models. Today a majority of advanced analytic applications use a primitive approach of moving data from databases into the application tier to derive intelligence. The integration of advanced analytics into a transactional database is revolutionary. A new platform for intelligent applications
#Download microsoft sql server 2016 express software
Software applications can now deploy sophisticated analytics and machine learning models in the database resulting in 100x or more speedup in time to insight, compared to deployments of such models outside of the database. Today we announced the general availability of SQL Server 2016, the world’s fastest and most price-performant database for HTAP (Hybrid Transactional and Analytical Processing) with updateable, in-memory columnstores and advanced analytics through deep integration with R Services. This post was authored by Joseph Sirosh, Corporate Vice President, Data Group, Microsoft.