Enterprises today understand the value of employing a master data management (MDM) solution for managing and governing mission critical information assets. chief data officers and chief information officers drive MDM initiatives with IBM® InfoSphere® Master Data Management to improve business results and operational efficiencies, which can help to lower costs and to reduce the risk of using untrusted master information in business process. Cloud computing introduces new considerations where enterprise IT architectures are extended beyond the corporate networks into the cloud.
Many enterprises are now adopting turnkey business applications offered as software as a service (SaaS) solutions, such as customer relationship management (CRM), payroll processing, human resource management, and many more. However, in the context of MDM solutions, many organizations perceive risks in having these solutions deployed on the cloud. In some cases, organization are concerned with the legal restrictions of deploying solutions on the cloud, whereas in other cases organizations have policies and strategies in force that limit solution deployment on the cloud.
Immaterial of what all the cases might be, industry trends point to a prediction that many "extended enterprises" will keep MDM solutions on premises and will want its integrations with SaaS applications, specifically customer and asset domains. This trend puts a key focus on an important component in the solution construct, that is, the cloud integration middleware and how it fits with hybrid cloud architectures that span on premises and cloud services. As this trend pans out, the on-premises MDM solution integration with SaaS applications will be the key pain point for the "extended enterprise."
Table of Contents
Chapter 1: Motivation for Big Data
Chapter 2: Hadoop Concepts
Chapter 3: Getting Started with the Hadoop Framework
Chapter 4: Hadoop Administration
Chapter 5: Basics of MapReduce Development
Chapter 6: Advanced MapReduce Development
Chapter 7: Hadoop Input/Output
Chapter 8: Testing Hadoop Programs
Chapter 9: Monitoring Hadoop
Chapter 10: Data Warehousing Using Hadoop
Chapter 11: Data Processing Using Pig
Chapter 12: HCatalog and Hadoop in the Enterprise
Chapter 13: Log Analysis Using Hadoop
Chapter 14: Building Real-Time Systems Using HBase
Chapter 15: Data Science with Hadoop
Chapter 16: Hadoop in the Cloud
Chapter 17: Building a YARN Application
Appendix A: Installing Hadoop
Appendix B: Using Maven with Eclipse
Appendix C: Apache Ambari