Data Science with Microsoft SQL Server 2016

The world around us, every business and nearly every industry, is being transformed by technology. This disruption is driven, in part, by the intersection of three trends: a massive explosion of data, intelligence from machine learning and advanced analytics, and the economics and agility of cloud computing.
While databases power nearly every aspect of business today, they were not originally designed with this disruption in mind. Traditional databases were about recording and retrieving transactions such as orders and payments very reliably, very securely and efficiently. They were designed to enable reliable, secure, mission-critical transactional applications at small to medium scale, in on-premises datacenters.

Databases built to get ahead of today’s disruptions do very fast analyses of live data in-memory as transactions are being recorded or queried. They support very low latency advanced analytics and machine learning, such as forecasting and predictive models, on the same data, so that applications can easily embed data-driven intelligence. They allow databases to be offered as a fully managed service in the cloud, in turn making it easy to build and deploy intelligent Software as a Service (SaaS) apps.

They also provide innovative security features built for a world where a majority of data is accessible over the Internet. They support 24×7 high-availability, efficient management and database administration across platforms. They therefore enable mission critical intelligent applications to be built and managed both in the cloud and on-premises. They are exciting harbingers of a new world of ambient intelligence.

SQL Server 2016 was built for this new world, and to help businesses get ahead of today’s disruptions. It supports hybrid transactional/analytical processing, advanced analytics and machine learning, mobile BI, data integration, always encrypted query processing capabilities and in-memory transactions with persistence. It integrates advanced analytics into the database, providing revolutionary capabilities to build intelligent, high performance transactional applications.

Imagine a core enterprise application built with a database such as SQL Server. What if you could embed intelligence, i.e. advanced analytics algorithms plus data transformations, within the database itself, to make every transaction intelligent in real time? That’s now possible for the first time with R and machine learning built into SQL Server 2016. By combining the performance of SQL Server in-memory OLTP technology as well as in-memory columnstores with R and machine learning, applications can get extraordinary analytical performance in production, as well as the throughput, parallelism, security, reliability, compliance certifications and manageability of an industrial strength database engine.

This book is the first to truly describe how you can create intelligence applications leveraging SQL Server and R. It is an exciting book that will empower every developer to unleash the power of data driven intelligence in their organization.

Introduction
R is one of the most popular, powerful data analytics languages and environments in use by data scientists. Actionable business data is often stored in Relational Database Management Systems (RDBMS), and one of the most widely used RDBMS is Microsoft SQL Server. Much more than a database server, it’s a rich ecostructure with advanced analytic capabilities. Microsoft SQL Server R Services combines these environments, allowing direct interaction between the data on the RDBMS and the R language, all while preserving the security and safety the RDBMS contains. In this book, you’ll learn how Microsoft has combined these two environments, how a data scientist can use this new capability, and practical, hands-on examples of using SQL Server R Services to create real-world solutions.