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ISBN10: 0071745327 | ISBN13: 9780071745321

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Publisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product. Develop a custom, agile data warehousing and business intelligence architecture Empower your users and drive better decision making across your enterprise with detailed instructions and best practices from an expert developer and trainer. The Data Warehouse Mentor: Practical Data Warehouse and Business Intelligence Insights shows how to plan, design, construct, and administer an integrated end-to-end DW/BI solution. Learn how to choose appropriate components, build an enterprise data model, configure data marts and data warehouses, establish data flow, and mitigate risk. Change management, data governance, and security are also covered in this comprehensive guide. Understand the components of BI and data warehouse systems Establish project goals and implement an effective deployment plan Build accurate logical and physical enterprise data models Gain insight into your company's transactions with data mining Input, cleanse, and normalize data using ETL (Extract, Transform, and Load) techniques Use structured input files to define data requirements Employ top-down, bottom-up, and hybrid design methodologies Handle security and optimize performance using data governance tools Robert Laberge is the founder of several Internet ventures and a principle consultant for the IBM Industry Models and Assets Lab, which has a focus on data warehousing and business intelligence solutions.
Chapter 1: Data Warehouse and Business Intelligence Overview
Chapter 2: Data in the Organization
Chapter 3: Reasons for Building
Chapter 4: Business Intelligence and Data Warehouse Strategy
Chapter 5: Project Resources: Roles and Insights
Chapter 6: Write-It-Up Overview
Part II: Components
Chapter 7: Business Intelligence: Data Marts and Usage
Chapter 8: Enterprise Data Models
Chapter 9: Data Warehouse Architecture: Components
Chapter 10: ETL and Data Quality
Chapter 11: Project Planning and Methodology
Part III: Let's Build
Chapter 12: Working Scenarios
Chapter 13: Data Governance
Chapter 14: Post-Project Review
Index
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