Book Image

Introducing Microsoft SQL Server 2019

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

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)

Using the team data science process with Machine Learning Services

You've explored the basics of machine learning, and you understand the languages, tools, and SQL Server 2019 components you can use to implement it, and now you're ready to get started on some actual data science. A data science project is different from traditional software development projects because it involves a single solution at a time, it is highly dependent on improving the solution once it is deployed, and it involves more stakeholders in the design and implementation.

In business intelligence, you can build a single cube that can answer many questions. But in data science, you can't use a k-means algorithm on a prediction that requires linear regression, and the features and labels needed for each would be entirely different – each question you want to answer requires a new project. Some will be small, others will be more involved, but all of them require that you work as a team...