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)

Data virtualization use cases

In this section, you will review three specific scenarios where a modern enterprise data hub implemented using data virtualization technology adds significant value to your solution.

Data virtualization and hybrid transactional analytical processing

One approach that has gained popularity in recent times is operational analytics, also known as hybrid transactional analytical processing (HTAP). With this approach, you blend the operational workload and the analytical workload into a single system for that dataset. This has the advantage of consolidation and can limit data duplication. It also addresses data quality issues at the source, which leads to a reduction in the data management burden. However, there is a notable downside. Most enterprises have multiple-source systems, which would result in multiple HTAP systems. This introduces the challenge to users of querying across all their analytical data.

Enter your modern enterprise data hub....