Data Warehousing in the Age of Big Data (The Morgan Kaufmann Series on Business Intelligence) - Tapa blanda

"This book argues that big data, with its three key dimensions of volume, velocity, and variety, presents a challenge that data analysts can’t ignore...the author considers how new technologies such as cloud computing, data virtualization, and solid-state drives (SSDs) will affect data warehousing...Overall, I found the book easy to read and understand. It’s written from the perspective of a practitioner; as such, it is meant for a hands-on person."--ComputingReviews.com, December 13, 2013 "Many times people confuse Big Data as a replacement for the data warehouse, but that is not true... and this book will help you and your organization understand how the two can co-exist...If your organization has established data warehouses and is looking to embrace Big Data, look no further than Data Warehousing in the Age of Big Data for advice on how to succeed in those efforts."--Data and Technology Today blog, August 8, 2013 "Krishnan, an expert on data warehousing, explains how Web 2.0 (e.g., Google, Facebook, Groupon) has transformed the way business is conducted. Krishnan traces the emergence of the data warehouse and discusses its technologies, processing architectures and challenges, and how to integrate big data and data warehousing."--Reference & Research Book News, October 2013 "Content-wise, the book targets two audiences. Readers coming from a data warehousing background will learn where big data fits in and how specific challenges can be addressed. For readers working in a big data community, the book will be very valuable for understanding the link between big data and data warehousing. For both groups, the book is an excellent and welcome addition to the literature."--ComputingReviews.com, September 20, 2013 "Data Warehousing in the Age of Big Data is an updated look at the seminal data store of our time, the data warehouse, and how it juxtaposes with the tsunami that is big data.  Ripe with relatable examples and perfect for updating core data warehouse knowledge, Krishnan has delivered the guide to not just data success, but business success, in this era of competition on information."--William McKnight, President, McKnight Consulting Group "Krish Krishnan has written the definitive book on Big Data. When it comes to understanding the technology, its implementation, and the actual achievement of business value, this book is THE place to look."--Bill Inmon, Forest Rim Technology

Data Warehousing in the Age of the Big Data will help you and your organization make the most of unstructured data with your existing data warehouse.

As Big Data continues to revolutionize how we use data, it doesn't have to create more confusion. Expert author Krish Krishnan helps you make sense of how Big Data fits into the world of data warehousing in clear and concise detail. The book is presented in three distinct parts. Part 1 discusses Big Data, its technologies and use cases from early adopters. Part 2 addresses data warehousing, its shortcomings, and new architecture options, workloads, and integration techniques for Big Data and the data warehouse. Part 3 deals with data governance, data visualization, information life-cycle management, data scientists, and implementing a Big Data–ready data warehouse. Extensive appendixes include case studies from vendor implementations and a special segment on how we can build a healthcare information factory.

Ultimately, this book will help you navigate through the complex layers of Big Data and data warehousing while providing you information on how to effectively think about using all these technologies and the architectures to design the next-generation data warehouse.

• Learn how to leverage Big Data by effectively integrating it into your data warehouse.
• Includes real-world examples and use cases that clearly demonstrate Hadoop, NoSQL, HBASE, Hive, and other Big Data technologies
• Understand how to optimize and tune your current data warehouse infrastructure and integrate newer infrastructure matching data processing workloads and requirements