Data Warehousing in the Age of Big Data
- 1st Edition - May 2, 2013
- Imprint: Morgan Kaufmann
- Author: Krish Krishnan
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 4 0 5 8 9 1 - 0
- eBook ISBN:9 7 8 - 0 - 1 2 - 4 0 5 9 2 0 - 7
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 rev… Read more
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Request a sales quoteData 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
Technical/Enterprise architects, Data Warehouse & Big Data professionals, developers, managers and business analysts.
Dedication
Acknowledgments
About the Author
Introduction
Part 1: Big Data
Part 2: The Data Warehousing
Part 3: Building the Big Data – Data Warehouse
Appendixes
Companion website
Part 1: Big Data
Chapter 1. Introduction to Big Data
Introduction
Big Data
Defining Big Data
Why Big Data and why now?
Big Data example
Summary
Further reading
Chapter 2. Working with Big Data
Introduction
Data explosion
Data volume
Data velocity
Data variety
Summary
Chapter 3. Big Data Processing Architectures
Introduction
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
Introduction
Distributed data processing
Big Data processing requirements
Technologies for Big Data processing
Hadoop
NoSQL
Textual ETL processing
Further reading
Chapter 5. Big Data Driving Business Value
Introduction
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
Summary
Part 2: The Data Warehousing
Chapter 6. Data Warehousing Revisited
Introduction
Traditional data warehousing, or data warehousing 1.0
Data warehouse 2.0
Summary
Further reading
Chapter 7. Reengineering the Data Warehouse
Introduction
Enterprise data warehouse platform
Choices for reengineering the data warehouse
Modernizing the data warehouse
Case study of data warehouse modernization
Summary
Chapter 8. Workload Management in the Data Warehouse
Introduction
Current state
Defining workloads
Understanding workloads
Query classification
ETL and CDC workloads
Measurement
Current system design limitations
New workloads and Big Data
Technology choices
Summary
Chapter 9. New Technologies Applied to Data Warehousing
Introduction
Data warehouse challenges revisited
Data warehouse appliance
Cloud computing
Data virtualization
Summary
Further reading
Part 3: Building the Big Data – Data Warehouse
Chapter 10. Integration of Big Data and Data Warehousing
Introduction
Components of the new data warehouse
Integration strategies
Hadoop & RDBMS
Big Data appliances
Data virtualization
Semantic framework
Summary
Chapter 11. Data-Driven Architecture for Big Data
Introduction
Metadata
Master data management
Processing data in the data warehouse
Processing complexity of Big Data
Machine learning
Summary
Chapter 12. Information Management and Life Cycle for Big Data
Introduction
Information life-cycle management
Information life-cycle management for Big Data
Summary
Chapter 13. Big Data Analytics, Visualization, and Data Scientists
Introduction
Big Data analytics
Data discovery
Visualization
The evolving role of data scientists
Summary
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
Summary
Appendix A. Customer Case Studies
Introduction
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
Introduction
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
Metadata
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
Summary
Further reading
Summary
Index
- Edition: 1
- Published: May 2, 2013
- No. of pages (Paperback): 370
- No. of pages (eBook): 370
- Imprint: Morgan Kaufmann
- Language: English
- Paperback ISBN: 9780124058910
- eBook ISBN: 9780124059207
KK
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, USARead Data Warehousing in the Age of Big Data on ScienceDirect