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  • Book Overview & Buying Introducing Microsoft SQL Server 2019
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Introducing Microsoft SQL Server 2019

Introducing Microsoft SQL Server 2019

By : Kellyn Gorman , Allan Hirt , Dave Noderer , Mitchell Pearson , James Rowland-Jones , Dustin Ryan , Arun Sirpal , Buck Woody
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Introducing Microsoft SQL Server 2019

Introducing Microsoft SQL Server 2019

By: Kellyn Gorman , Allan Hirt , Dave Noderer , Mitchell Pearson , James Rowland-Jones , Dustin Ryan , Arun Sirpal , Buck Woody

Overview of this book

Microsoft SQL Server comes equipped with industry-leading features and the best online transaction processing capabilities. If you are looking to work with data processing and management, getting up to speed with Microsoft Server 2019 is key. Introducing SQL Server 2019 takes you through the latest features in SQL Server 2019 and their importance. You will learn to unlock faster querying speeds and understand how to leverage the new and improved security features to build robust data management solutions. Further chapters will assist you with integrating, managing, and analyzing all data, including relational, NoSQL, and unstructured big data using SQL Server 2019. Dedicated sections in the book will also demonstrate how you can use SQL Server 2019 to leverage data processing platforms, such as Apache Hadoop and Spark, and containerization technologies like Docker and Kubernetes to control your data and efficiently monitor it. By the end of this book, you'll be well versed with all the features of Microsoft SQL Server 2019 and understand how to use them confidently to build robust data management solutions.
Table of Contents (15 chapters)
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Machine learning using the PREDICT T-SQL command

Once you have created a model (also called a "trained model"), you can save it in a binary format for "scoring" the results of a prediction. Both R and Python have methods to store the trained models as binary outputs. It's common to store these models in the database itself, which then allows you to process requests from clients in a T-SQL statement and return the results as a dataset. This process requires the runtime (R or Python) to process the request.

As an example, if you were to create the k-means clustering solution around the customer returns mentioned earlier, you could save that model as a binary object, perhaps even from another server that holds the customer data. You could then deploy that model to a server located at each store and run it using the PREDICT statement to alert the salespeople to the behavior that might lead to a return, thereby preventing customer dissatisfaction.

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