Data Warehousing in the Age of Big Data

Data Warehousing in the Age of Big Data

1st Edition - May 2, 2013

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  • Author: Krish Krishnan
  • Paperback ISBN: 9780124058910
  • eBook ISBN: 9780124059207

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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.

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


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

Table of Contents

  • Dedication


    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



Product details

  • No. of pages: 370
  • Language: English
  • Copyright: © Morgan Kaufmann 2013
  • Published: May 2, 2013
  • Imprint: Morgan Kaufmann
  • Paperback ISBN: 9780124058910
  • eBook ISBN: 9780124059207

About the Author

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, USA

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