Microsoft Business Intelligence Tools for Excel Analysts
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Copyright 2014 by John Wiley & Sons, Inc., Hoboken, New Jersey
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Library of Congress Control Number: 2013954104
ISBN 978-1-118-82152-7 (pbk); ISBN 978-1-118-82156-5 (ebk); ISBN 978-1-118-82155-8 (ebk)
Manufactured in the United States of America
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About the Authors
Mike Alexander is a Microsoft Certified Application Developer (MCAD) and author of several books on advanced business analysis with Microsoft Access and Excel. He has more than 16 years experience consulting and developing Office solutions. Mike has been named a Microsoft MVP for his ongoing contributions to the Excel community. You can visit Mike at www.datapigtechnologies.com , where he regularly shares Excel and Access tips and techniques.
Jared Decker has over fourteen years of experience in the IT industry and ten years of consulting experience focused exclusively on data warehousing and business intelligence. In addition to playing an architect or lead role on dozens of projects, he has spent more than five hundred hours in-house with corporations training their development teams on the Microsoft SQL Server, Tableau, and QlikView BI platforms. His breadth of experience entails everything from architecture and design to system implementation, with particular focus on business analytics and data visualization. Jared holds technical certifications in Microsoft (MCITP Business Intelligence Developer and certified trainer), Tableau Developer, and QlikView Developer and Trainer.
Bernard Wehbe has over 14 years of consulting experience focused exclusively on data warehousing, analytics, and business intelligence. His experience includes data warehousing architecture, OLAP, data modeling, ETL, reporting, business analysis, team leadership, and project management. Prior to founding StatSlice Systems, Bernard served as a technical architect for Hitachi Consulting in the Dallas, TX area.
Publishers Acknowlegments
Sr. Acquisitions Editor: Katie Mohr
Project Editor: Rebecca Senninger
Copy Editor: Lynn Northrup
Technical Editor: Mike Talley
Editorial Assistant: Anne Sullivan
Sr. Editorial Assistant: Cherie Case
Project Coordinator: Patrick Redmond
APPENDIX A: Understanding the Big Data Toolset
Big data refers to a broad set of tools that are designed to work with large data volumes. These tools are gaining popularity because they solve the limitations of traditional tools by scaling up on hardware and sharing the workload across many linked computers. Using this divide-and-conquer approach allows big data technologies to accomplish tasks that are not feasible otherwise.
When it comes to analytics, there are some special considerations regarding big data. You need an easy way to access and navigate platforms without the need to spend precious time learning new programming languages such as Java. In the past few years several tools have come available that offer a big data SQL solution: a tool that sits on top of a big data platform and allows you to write SQL queries against the big data engine.
In this appendix we review the big data SQL tools on the market now, giving you a sense of the big data landscape. Youll also discover how you can connect to those tools from Excel.
Big Data SQL Offerings
There are several big data offerings that support a relatively easy-to-use SQL engine on top of Hadoop or another similar-style platform.
We cover the talked-about entrants into the market as of this writing. Note, though, that this space is quite new and many companies are jumping into it almost daily.
Most of these technologies have a similar approach to big data SQL. They scale up on hardware and use a divide-and-conquer approach to break big data volumes into small chunks and distribute the processing across multiple nodes. The main differentiator among the various technologies is whether or not they are Hadoop-based. For example, Amazon Redshift does not sit on top of Hadoop, but instead uses a different technology that allows similar scalability as Hadoop. On the other hand, Hortonworks and Cloudera are tools that are installed on top of Hadoop and take advantage of its scalability features.
Amazon Redshift
Provider:
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