Data integration blueprint and modeling techniques for a scalable and sustainable architecture
Making Data Integration Work: How to Systematically Reduce Cost, Improve Quality, and Enhance Effectiveness Today’s enterprises are investing massive resources in data integration. Many possess thousands of point-to-point data integration applications that are costly, undocumented, and difficult to...
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| Corporate Author: | |
| Format: | eBook |
| Language: | Inglés |
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Upper Saddle River, N.J. :
IBM Press/Pearson
c2011
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| Edition: | 1st edition |
| Subjects: | |
| See on Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009629004206719 |
Table of Contents:
- Cover
- Contents
- Preface
- Acknowledgments
- About the Author
- Introduction: Why Is Data Integration Important?
- Part 1 Overview of Data Integration
- Chapter 1 Types of Data Integration
- Data Integration Architectural Patterns
- Common Data Integration Functionality
- Summary
- End-of-Chapter Questions
- Chapter 2 An Architecture for Data Integration
- What Is Reference Architecture?
- Reference Architecture for Data Integration
- The Layers of the Data Integration Architecture
- Extract/Subscribe Processes
- Initial Staging Landing Zone
- Data Quality Processes
- Clean Staging Landing Zone
- Transform Processes
- Load-Ready Publish Landing Zone
- Load/Publish Processes
- An Overall Data Architecture
- Summary
- End-of-Chapter Questions
- Chapter 3 A Design Technique: Data Integration Modeling
- The Business Case for a New Design Process
- Improving the Development Process
- Overview of Data Integration Modeling
- Conceptual Data Integration Models
- Logical Data Integration Models
- Physical Data Integration Models
- Tools for Developing Data Integration Models
- Industry-Based Data Integration Models
- Summary
- End-of-Chapter Questions
- Chapter 4 Case Study: Customer Loan Data Warehouse Project
- Case Study Overview
- Step 1: Build a Conceptual Data Integration Model
- Step 2: Build a High-Level Logical Model Data Integration Model
- Step 3: Build the Logical Extract DI Models
- Step 4: Define a Logical Data Quality DI Model
- Step 5: Define the Logical Transform DI Model
- Step 6: Define the Logical Load DI Model
- Step 7: Determine the Physicalization Strategy
- Step 8: Convert the Logical Extract Models into Physical Source System Extract DI Models
- Step 9: Refine the Logical Load Models into Physical Source System Subject Area Load DI Models.
- Step 10: Package the Enterprise Business Rules into Common Component Models
- Step 11: Sequence the Physical DI Models
- Summary
- Part 2 The Data Integration Systems Development Life Cycle
- Chapter 5 Data Integration Analysis
- Analyzing Data Integration Requirements
- Building a Conceptual Data Integration Model
- Performing Source System Data Profiling
- Reviewing/Assessing Source Data Quality
- Performing Source\Target Data Mappings
- Summary
- End-of-Chapter Questions
- Chapter 6 Data Integration Analysis Case Study
- Case Study Overview
- Data Integration Analysis Phase
- Summary
- Chapter 7 Data Integration Logical Design
- Determining High-Level Data Volumetrics
- Establishing a Data Integration Architecture
- Identifying Data Quality Criteria
- Creating Logical Data Integration Models
- Defining One-Time Data Conversion Load Logical Design
- Summary
- End-of-Chapter Questions
- Chapter 8 Data Integration Logical Design Case Study
- Step 1: Determine High-Level Data Volumetrics
- Step 2: Establish the Data Integration Architecture
- Step 3: Identify Data Quality Criteria
- Step 4: Create Logical Data Integration Models
- Summary
- Chapter 9 Data Integration Physical Design
- Creating Component-Based Physical Designs
- Preparing the DI Development Environment
- Creating Physical Data Integration Models
- Designing Parallelism into the Data Integration Models
- Designing Change Data Capture
- Finalizing the History Conversion Design
- Defining Data Integration Operational Requirements
- Designing Data Integration Components for SOA
- Summary
- End-of-Chapter Questions
- Chapter 10 Data Integration Physical Design Case Study
- Step 1: Create Physical Data Integration Models
- Step 2: Find Opportunities to Tune through Parallel Processing
- Step 3: Complete Wheeler History Conversion Design.
- Step 4: Define Data Integration Operational Requirements
- Developing a Job Schedule for Wheeler
- Summary
- Chapter 11 Data Integration Development Cycle
- Performing General Data Integration Development Activities
- Prototyping a Set of Data Integration Functionality
- Completing/Extending Data Integration Job Code
- Performing Data Integration Testing
- The Role of Configuration Management in Data Integration
- Summary
- End-of-Chapter Questions
- Chapter 12 Data Integration Development Cycle Case Study
- Step 1: Prototype the Common Customer Key
- Step 2: Develop User Test Cases
- Summary
- Part 3 Data Integration with Other Information Management Disciplines
- Chapter 13 Data Integration and Data Governance
- What Is Data Governance?
- Why Is Data Governance Important?
- Components of Data Governance
- Summary
- End-of-Chapter Questions
- Chapter 14 Metadata
- What Is Metadata?
- The Role of Metadata in Data Integration
- Categories of Metadata
- Metadata as Part of a Reference Architecture
- Metadata Users
- Managing Metadata
- Summary
- End-of-Chapter Questions
- Chapter 15 Data Quality
- The Data Quality Framework
- The Data Quality Life Cycle
- The Define Phase
- The Audit Phase
- The Renovate Phase
- Final Thoughts on Data Quality
- Summary
- End-of-Chapter Questions
- Appendix A: Exercise Answers
- Appendix B: Data Integration Guiding Principles
- Write Once, Read Many
- Grab Everything
- Data Quality before Transforms
- Transformation Componentization
- Where to Perform Aggregations and Calculations
- Data Integration Environment Volumetric Sizing
- Subject Area Volumetric Sizing
- Appendix C: Glossary
- Index.
