Data Warehousing in the Age of Big Data

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

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

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