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.
The integration of advanced analytics into a transactional database is 'revolutionary'. Today a majority of advanced analytic applications use a primitive approach of moving data from databases into the application tier to derive intelligence. 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. And deep analytics on real-time transactions are next to impossible without a lot of heavy lifting.
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. 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. As a result, analytical applications can now be far simpler and need only query the database for analytic results. Updating machine learning models, deploying new models, and monitoring their performance can now be done in the database without recompiling and redeploying applications. Furthermore, the database can serve as a central server for the enterprise’s analytical models and multiple intelligent applications can leverage the same models. It is a profound simplification in how mission critical intelligent applications can be built and managed in the enterprise.
