Data Warehousing in the Age of Big Data

Data Warehousing in the Age of Big Data, 1st Edition

Data Warehousing in the Age of Big Data, 1st Edition,Krish Krishnan,ISBN9780124058910


Morgan Kaufmann




235 X 191

The first and only comprehensive guide to the effective use of Big Data and Data Warehousing!

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Key Features

  • 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


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.


Technical/Enterprise architects, Data Warehouse & Big Data professionals, developers, managers and business analysts.

Krish Krishnan

Krish Krishnan is a recognized expert worldwide in the strategy, architecture and implementation of high performance data warehousing solutions and unstructured Data. A sought after visionary data warehouse thought leader and practitioner, he is ranked as one of the top strategy and architecture consultants in the world in this subject. Krish is also an independent analyst, and a speaker at various conferences around the world on Big Data and teaches at TDWI on this subject. Krish along with other experts is helping drive the industry maturity on the next generation of data warehousing, focusing on Big Data, Semantic Technologies, Crowdsourcing, Analytics, and Platform Engineering. Krish is the founder president of Sixth Sense Advisors Inc., a Chicago based company providing Independent Analyst services in Big Data, Analytics, Data Warehouse and Business Intelligence.

Affiliations and Expertise

Founder and President of Sixth Sense Advisors, Inc., Chicago, Illinois

Data Warehousing in the Age of Big Data, 1st Edition



About the Author


Part 1: Big Data

Part 2: The Data Warehousing

Part 3: Building the Big Data – Data Warehouse


Companion website

Part 1: Big Data

Chapter 1. Introduction to Big Data


Big Data

Defining Big Data

Why Big Data and why now?

Big Data example


Further reading

Chapter 2. Working with Big Data


Data explosion

Data volume

Data velocity

Data variety


Chapter 3. Big Data Processing Architectures


Data processing revisited

Data processing techniques

Data processing infrastructure challenges

Shared-everything and shared-nothing architectures

Big Data processing

Telco Big Data study

Chapter 4. Introducing Big Data Technologies


Distributed data processing

Big Data processing requirements

Technologies for Big Data processing



Textual ETL processing

Further reading

Chapter 5. Big Data Driving Business Value


Case study 1: Sensor data

Case study 2: Streaming data

Case study 3: The right prescription: improving patient outcomes with Big Data analytics

Case study 4: University of Ontario, institute of technology: leveraging key data to provide proactive patient care

Case study 5: Microsoft SQL server customer solution

Case study 6: Customer-centric data integration


Part 2: The Data Warehousing

Chapter 6. Data Warehousing Revisited


Traditional data warehousing, or data warehousing 1.0

Data warehouse 2.0


Further reading

Chapter 7. Reengineering the Data Warehouse


Enterprise data warehouse platform

Choices for reengineering the data warehouse

Modernizing the data warehouse

Case study of data warehouse modernization


Chapter 8. Workload Management in the Data Warehouse


Current state

Defining workloads

Understanding workloads

Query classification

ETL and CDC workloads


Current system design limitations

New workloads and Big Data

Technology choices


Chapter 9. New Technologies Applied to Data Warehousing


Data warehouse challenges revisited

Data warehouse appliance

Cloud computing

Data virtualization


Further reading

Part 3: Building the Big Data – Data Warehouse

Chapter 10. Integration of Big Data and Data Warehousing


Components of the new data warehouse

Integration strategies

Hadoop & RDBMS

Big Data appliances

Data virtualization

Semantic framework


Chapter 11. Data-Driven Architecture for Big Data



Master data management

Processing data in the data warehouse

Processing complexity of Big Data

Machine learning


Chapter 12. Information Management and Life Cycle for Big Data


Information life-cycle management

Information life-cycle management for Big Data


Chapter 13. Big Data Analytics, Visualization, and Data Scientists


Big Data analytics

Data discovery


The evolving role of data scientists


Chapter 14. Implementing the Big Data – Data Warehouse – Real-Life Situations

Introduction: Building the Big Data – Data Warehouse

Customer-centric business transformation

Hadoop and MySQL drives innovation

Integrating Big Data into the data warehouse


Appendix A. Customer Case Studies


Case study 1: Transforming marketing landscape

Case study 2: Streamlining healthcare connectivity with Big Data

Case study 3: Improving healthcare quality and costs using Big Data

Case study 4: Improving customer support

Case study 5: Driving customer-centric transformations

Case study 6: Quantifying risk and compliance

Case study 7: Delivering a 360° view of customers

Appendix B. Building the Healthcare Information Factory: Healthcare Information Factory: Implementing Textual Analytics


Executive summary

The healthcare information factory

A visionary architecture

Separate systems

A common patient identifier

Integrating data

The larger issue of integration across many data types

ETL and the collective common data warehouse

Common elements of a data warehouse

Analytical processing

DSS/business intelligence processing

Different types of data that go into the data warehouse

Textual data

The system of record


Local individual data warehouses

Data models and the healthcare information factory

Creating the medical data warehouse data model

The collective common data model

Developing the healthcare information factory

Healthcare information factory users

Other healthcare entities

Financing the infrastructure

The age of data in the healthcare information factory

Implementing the healthcare information factory


Further reading



Quotes and reviews

"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

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