Analytics Optimization with Columnstore Indexes in Microsoft SQL Server - Second Edition - Cost-Effective, Scalable Analytics for Real-World SQL Server Workloads

Oct 18, 2026

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Analytics Optimization with Columnstore Indexes in Microsoft SQL Server (Second Edition) focuses on cost-effective and scalable analytics for real-world SQL Server workloads. The material guides readers through where analytic data should reside, how analytic data size and structure impact system design, and how scaling decisions are made. It also compares structured approaches like data warehouse and unstructured approaches like data lake, helping practitioners choose sources and strategies aligned with performance goals.

LanguageEnglish

CategoryTechnology

FormatPDF

Pages320

Reading Time13 h 26 min

AccessFree to download

About the Author

About the Technical Reviewer

Acknowledgments

Introduction

Chapter 1: Introduction to Analytic Data in a Transactional Database

Where Should Analytic Data Reside?

Analytic Data Size

Analytic Data Structure

Data Scale

Vertical Scaling

Horizontal Scaling

Scaling Decisions and Strategy

Analytic Data Sources

Analytics Optimization with Columnstore Indexes in Microsoft SQL Server Cost-Effective, Scalable Analytics for Real-World SQL Server Workloads — Second Edition — Edward Pollack Analytics Optimization with Columnstore Indexes in Microsoft SQL Server Cost-Effective, Scalable Analytics for Real-World SQL Server Workloads Second Edition Edward Pollack Analytics Optimization with Columnstore Indexes in Microsoft SQL Server: Cost-Effective, Scalable Analytics for Real-World SQL Server Workloads, Second Edition Edward Pollack Albany, NY, USA ISBN-13 (pbk): 979-8-8688-2609-2 ISBN-13 (electronic): 979-8-8688-2610-8 [https://doi.org/10.1007/979-8-8688-2610-8](https://doi.org/10.1007/979-8-8688-2610-8) Copyright © 2026 by Edward Pollack This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. Trademarked names, logos, and images may appear in this book. Rather than use a trademark symbol with every occurrence of a trademarked name, logo, or image we use the names, logos, and images only in an editorial fashion and to the benefit of the trademark owner, with no intention of infringement of the trademark. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. While the advice and information in this book are believed to be true and accurate at the date of publication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors oromissions that maybe made. The publisher makes no warranty, express or implied, with respect to the material contained herein. Managing Director, Apress Media LLC: Welmoed Spahr Acquisitions Editor: Shaul Elson Editorial Project Manager: Gryffin Winkler Cover designed by eStudioCalamar Cover image designed by GarryKillian on freepik Distributed to the book trade worldwide by Springer Science+Business Media New York, 1 New York Plaza, New York, NY 10004. Phone 1-800-SPRINGER, fax (201) 348-4505, [e-mail](e-mail orders-ny@springer-sbm.com)[ orders-ny@springer-sbm.com](e-mail orders-ny@springer-sbm.com), or [visit](visit www.springeronline.com. Apress Media)[ www.springeronline.com](visit www.springeronline.com. Apress Media)[. Apress Media](visit www.springeronline.com. Apress Media), LLC is a Delaware LLC and the sole member (owner) is Springer Science + Business Media Finance Inc (SSBM Finance Inc) . SSBM Finance Inc is a Delaware corporation. For information on translations, [please e-mail](please e-mail booktranslations@springernature.com)[ booktranslations@springernature.com](please e-mail booktranslations@springernature.com); for reprint, paperback, or audio rights, [please e-mail](please e-mail bookpermissions@springernature.com)[ bookpermissions@springernature.com](please e-mail bookpermissions@springernature.com). Apress titles may be purchased in bulk for academic, corporate, or promotional use. eBook versions and licenses are also available for most titles. For more information, reference our Print and eBook Bulk Sales web page at [http://www.apress.com/bulk-sales](http://www.apress.com/bulk-sales). Any source code or other supplementary material referenced by the author in this book is available to readers on GitHub. For more detailed information, please visit [https://www.apress.com/gp/services/](https://www.apress.com/gp/services/)[ ](https://www.apress.com/gp/services/)[source-code](source-code.)[.](source-code.) If disposing

FAQs

What topics does the book cover in Chapter 1?

It addresses where analytic data should reside, analytic data size and structure, data scale, vertical and horizontal scaling, and how scaling decisions and strategies are made. It also introduces analytic data sources and compares warehouse-style structured data with lake-style unstructured data.

How does the book frame structured versus unstructured analytics data sources?

Structured data is discussed in the context of a data warehouse, while unstructured data is discussed in the context of a data lake. This distinction helps readers choose approaches aligned with the nature of the analytics workload.

What is the main goal of the analytics optimization approach described in the title?

The approach targets cost-effective, scalable analytics for real-world SQL Server workloads by optimizing analytics using columnstore indexes in Microsoft SQL Server, particularly under different scaling and data organization considerations.

Oct 18, 2026

Upload by Rowan