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SQL Server 2016 Developer Edition Download
A new platform for intelligent applications
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, doesnt 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 enterprises 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.
A 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. PROS Holdings uses SQL Server 2016s superior performance and built-in R Service to deliver advanced analytics more than 100x faster than before, resulting in higher profits for their customers. Royce Kallesen, senior director of science and research at PROS says, œMicrosoft Rs parallelization and enhanced memory management on the server integrated with SQL Server provides dramatically faster results on a common platform with built-in security.?
Eliminating the need to move data out of the database for analytics dramatically reduces the latency for insights. 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.
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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