SQL Server Big Data Clusters: Early First Edition Based on Release Candidate 1

by Benjamin Weissman, Enrico Van De Laar

Get a head-start on learning one of SQL Server 2019’s latest and most impactful features—Big Data Clusters—that combines large volumes of non-relational data for analysis along with data stored relationally inside a SQL Server database. This book provides a first look at Big Data Clusters based upon SQL Server 2019 Release Candidate 1. Start now and get a jump on your competition in learning this important new feature. Big Data Clusters is a feature set covering data virtualization, distributed computing, and relational databases and provides a complete AI platform across the entire cluster environment. This book shows you how to deploy, manage, and use Big Data Clusters. For example, you will learn how to combine data stored on the HDFS file system together with data stored inside the SQL Server instances that make up the Big Data Cluster. Filled with clear examples and use cases, this book provides everything necessary to get started working with Big Data Clusters in SQL Server 2019 using Release Candidate 1. You will learn about the architectural foundations that are made up from Kubernetes, Spark, HDFS, and SQL Server on Linux. You then are shown how to configure and deploy Big Data Clusters in on-premises environments or in the cloud. Next, you are taught about querying. You will learn to write queries in Transact-SQL—taking advantage of skills you have honed for years—and with those queries you will be able to examine and analyze data from a wide variety of sources such as Apache Spark. Through the theoretical foundation provided in this book and easy-to-follow example scripts and notebooks, you will be ready to use and unveil the full potential of SQL Server 2019: combining different types of data spread across widely disparate sources into a single view that is useful for business intelligence and machine learning analysis. What You Will LearnInstall, manage, and troubleshoot Big Data Clusters in cloud or on-premise environmentsAnalyze large volumes of data directly from SQL Server and/or Apache SparkManage data stored in HDFS from SQL Server as if it were relational dataImplement advanced analytics solutions through machine learning and AIExpose different data sources as a single logical source using data virtualizationWho This Book Is ForFor data engineers, data scientists, data architects, and database administrators who want to employ data virtualization and big data analytics in their environment

315 pages, Kindle Edition

Published November 26, 2019

About the author

Benjamin Weissman is the author of two books of short fiction, Headless (2004) and Dear Dead Person (1994). His writing has appeared in Artforum, The Believer, Los Angeles Times, and McSweeney's. His collaboration with Yutaka Sone, What Every Snowflake Knows in Its Heart, was shown at Santa Monica Museum of Art. He teaches at Art Center College of Design in Pasadena, California, and Otis College of Art and Design in Los Angeles.
Born: 1957, Los Angeles, California, United States

Reviews

[John Pyrce · Rating 3 out of 5]

This is a decent book describing a new Microsoft SQL Server capability. I was amused that a second edition came out less than 6 months after the first edition: that's progress! Hopefully there weren't too many changes ....

The strength of the book is the step by step instructions on how to do scenarios. The scenarios use open source data that it readily available and let you follow along easily. Despite the detailed instructions on setting up a sample installation, I did have problems with doing that due to technical constraints. Not really the authors fault.

Outside of the detailed instructions, I found the overview material to be not quite as good as what Microsoft provides in their online documentation. Microsoft certainly draws prettier diagrams.

Some of the images in the book are almost illegible due to shrinking screen shots. I understand why this happens, but the lack of legibility in some cases was annoying.

So I would recommend this as a resource for doing scenarios to increase your understanding, augmented by other documentation.