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.
Listed here are a number of the noteworthy new functions in SQL Server 2016:
SQL Server 2016 comes with several features and tools to support cross-platform analytics. Polybase allows you to run queries on external data in Hadoop or Azure blob storage. It can push computation to Hadoop where appropriate, so that your analytical application can join and integrate data from big data stores with the data in the relational store. Microsoft R Services, which is integrated with SQL Server also runs on multiple Hadoop distributions and is also integrated with Azure HDInsight + Spark, enabling both choice and standardization in developing analytics code. And finally, R Tools for Visual Studio allows the ease of use of the modern Visual Studio IDE for developing analytical code in R.
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