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The Kimball Group Reader 2-Ed. Practical Tools for Data Warehousing and Business Intelligence-Bob Becker, Joy Mundy, Margy Ross, Ralph Kimball, Warren Thornthwaite
The Kimball Group Reader 2-Ed. Practical Tools for Data Warehousing and Business Intelligence  Review
by Bob Becker, Joy Mundy, Margy Ross, Ralph Kimball, Warren Thornthwaite (Author)
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Publisher : Wiley
Publish date : 25-Jan-2016
Category : Data Warehousing
Mediatype : Books
Binding : Paperback
Availability : IsAvailable, Order now ships within 5-7 business days
List Price :   Rs. 799
Greenleaf Price :  Rs. 719
You Save :  Rs. 80
ISBN : 8126559705 / Indian ISBN 13: 9788126559701
Pages : 912
Book Summary : The Kimball Group Reader 2-Ed. Practical Tools for Data Warehousing and Business Intelligence
The Kimball Group Reader, Remastered Collection is the essential reference for data warehouse and business intelligence design, packed with best practices, design tips, and valuable insight from industry pioneer Ralph Kimball and the Kimball Group. This Remastered Collection represents decades of expert advice and mentoring in data warehousing and business intelligence and is the final work to be published by the Kimball Group.

Table of Contents
1 The Reader at a Glance

1.1 Resist the Urge to Start Coding  
1.2 Set Your Boundaries
1.3 Data Wrangling
1.4 Myth Busters
1.5 Dividing the World
1.6 Essential Steps for the Integrated Enterprise Data Warehouse
1.7 Drill Down to Ask Why
1.8 Slowly Changing Dimensions
1.9 Judge Your BI Tool through Your Dimensions
1.10 Fact Tables
1.11 Exploit Your Fact Tables

2 Before You Dive In
2.1 History Lesson on Ralph Kimball and Xerox PARC
2.2 The Database Market Splits
2.3 Bringing Up Supermarts
2.4 Brave New Requirements for Data Warehousing
2.5 Coping with the Brave New Requirements
2.6 Stirring Things Up
2.7 Design Constraints and Unavoidable Realities
2.8 Two Powerful Ideas
2.9 Data Warehouse Dining Experience
2.10 Easier Approaches for Harder Problems
2.11 Expanding Boundaries of the Data Warehouse
3 Project / Program Planning
3.1 Professional Boundaries
3.2 An Engineer's View
3.3 Beware the Objection Removers
3.4 What Does the Central Team Do?
3.5 Avoid Isolating DW and BI Teams
3.6 Better Business Skills for BI and Data Warehouse Professionals
3.7 Risky Project Resources Are Risky Business
3.8 Implementation Analysis Paralysis
3.9 Contain DW/BI Scope Creep and Avoid Scope Theft
3.10 Are IT Procedures Beneficial to DW/BI Projects?
3.11 Habits of Effective Sponsors
3.12 TCO Starts with the End User
3.13 Kimball Lifecycle in a Nutshell
3.14 Off the Bench
3.15 The Anti-Architect
3.16 Think Critically When Applying Best Practices
3.17 Eight Guidelines for Low Risk Enterprise Data Warehousing

4 Requirements Definition
4.1 Alan Alda's Interviewing Tips for Uncovering Business Requirements
4.2 More Business Requirements Gathering Dos and Don'ts
4.3 Balancing Requirements and Realities
4.4 Overcoming Obstacles When Gathering Business Requirements
4.5 Surprising Value of Data Profiling
4.6 Focus on Business Processes, Not Business Departments!
4.7 Identifying Business Processes
4.8 Business Process Decoder Ring
4.9 Relationship between Strategic Business Initiatives and Business Processes
4.10 The Bottom-Up Misnomer
4.11 Think Dimensionally (Beyond Data Modeling)
4.12 Using the Dimensional Model to Validate Business Requirements
5 Data Architecture
5.1 Is ER Modeling Hazardous to DSS?
5.2 A Dimensional Modeling Manifesto
5.3 There Are No Guarantees
5.4 Divide and Conquer
5.5 The Matrix
5.6 The Matrix: Revisited
5.7 Drill Down into a Detailed Bus Matrix
5.8 Relating to Agile Methodologies
5.9 Is Agile Enterprise Data Warehousing an Oxymoron?
5.10 Going Agile? Start with the Bus Matrix
5.11 Conformed Dimensions as the Foundation for Agile Data Warehousing
5.12 Integration for Real People
5.13 Build a Ready-to-Go Resource for Enterprise Dimensions
5.14 Data Stewardship 101: The First Step to Quality and Consistency
5.15 To Be or Not To Be Centralized
5.16 Differences of Opinion
5.17 Much Ado about Nothing
5.18 Don't Support Business Intelligence with a Normalized EDW
5.19 Complementing 3NF EDWs with Dimensional Presentation Areas

6 Dimensional Modeling Fundamentals

6.1 Fact Tables and Dimension Tables
6.2 Drilling Down, Up and Across
6.3 The Soul of the Data Warehouse, Part One: Drilling Down
6.4 The Soul of the Data Warehouse, Part Two: Drilling Across
6.5 The Soul of the Data Warehouse, Part Three: Handling Time
6.6 Graceful Modifications to Existing Fact and Dimension Tables
6.7 Kimball's Ten Essential Rules of Dimensional Modeling
6.8 What Not to Do
6.9 Dangerous Preconceptions
6.10 Fables and Facts
7 Dimensional Modeling Tasks and Responsibilities
7.1 Letting the Users Sleep
7.2 Practical Steps for Designing a Dimensional Model
7.3 Staffing the Dimensional Modeling Team
7.4 Involve Business Representatives in Dimensional Modeling
7.5 Managing Large Dimensional Design Teams
7.6 Use a Design Charter to Keep Dimensional Modeling Activities on Track
7.7 The Naming Game
7.8 What's in a Name?
7.9 When Is the Dimensional Design Done?
7.10 Design Review Dos and Don'ts
7.11 Fistful of Flaws
7.12 Rating Your Dimensional Data Warehouse

8 Fact Table Core Concepts
8.1 Declaring the Grain
8.2 Keep to the Grain in Dimensional Modeling
8.3 Warning: Summary Data May Be Hazardous to Your Health
8.4 No Detail Too Small
8.5 Fundamental Grains
8.6 Modeling a Pipeline with an Accumulating Snapshot
8.7 Combining Periodic and Accumulating Snapshots
8.8 Complementary Fact Table Types
8.9 Modeling Time Spans
8.10 A Rolling Prediction of the Future, Now and in the Past
8.11 Timespan Accumulating Snapshot Fact Tables
8.12 Is it a Dimension, a Fact, or Both?
8.13 Factless Fact Tables
8.14 Factless Fact Tables? Sounds Like Jumbo Shrimp?
8.15 What Didn't Happen
8.16 Factless Fact Tables for Simplification
8.17 Managing Your Parents
8.18 Patterns to Avoid When Modeling Header / Line Item Transactions
8.19 Fact Table Surrogate Keys
8.20 Reader Suggestions on Fact Table Surrogate Keys
8.21 Another Look at Degenerate Dimensions
8.22 Creating a Reference Dimension for Infrequently Accessed Degenerates
8.23 Put Your Fact Tables on a Diet
8.24 Keeping Text Out of the Fact Table
8.25 Dealing with Nulls in a Dimensional Model
8.26 Modeling Data as Both a Fact and Dimension Attribute
8.27 When a Fact Table Can Be Used as a Dimension Table
8.28 Sparse Facts and Facts with Short Lifetimes
8.29 Pivoting the Fact Table with a Fact Dimension
8.30 Accumulating Snapshots for Complex Workflows

9 Dimension Table Core Concepts
9.1 Surrogate Keys
9.2 Keep Your Keys Simple
9.3 Durable "Super-Natural" Keys
9.4 It's Time for Time
9.5 Surrogate Keys for the Time Dimension
9.6 Latest Thinking on Time Dimension Tables
9.7 Smart Date Keys to Partition Fact Tables
9.8 Updating the Date Dimension
9.9 Handling All the Dates
9.10 Selecting Default Values for Nulls
9.11 Data Warehouse Role Models
9.12 Mystery Dimensions
9.13 De-Clutter with Junk Dimensions
9.14 Showing the Correlation between Dimensions
9.15 Causal (Not Casual) Dimensions
9.16 Resist Abstract Generic Dimensions
9.17 Hot-Swappable Dimensions
9.18 Accurate Counting with a Dimensional Supplement
9.19 Perfectly Partitioning History with Type 2 SCD
9.20 Many Alternate Realities
9.21 Monster Dimensions
9.22 When a Slowly Changing Dimension Speeds Up
9.23 When Do Dimensions Become Dangerous?
9.24 Slowly Changing Dimensions Are Not Always as Easy as 1, 2 and 3
9.25 Slowly Changing Dimension Types 0, 4, 5, 6 and 7
9.26 Dimension Row Change Reason Attributes

10 More Dimension Patterns and Considerations
10.1 Snowflakes, Outriggers and Bridges
10.2 A Trio of Interesting Snowflakes
10.3 Help for Dimensional Modeling
10.4 Managing Bridge Tables
10.5 The Keyword Dimension
10.6 Potential Bridge (Table) Detours
10.7 Alternatives for Multi-Valued Dimensions
10.8 Adding a Mini-Dimension to a Bridge Table
10.9 Maintaining Dimension Hierarchies
10.10 Help for Hierarchies
10.11 Five Alternatives for Better Employee Dimensional Modeling
10.12 Avoiding Alternate Organization Hierarchies
10.13 Alternate Hierarchies
10.14 Dimension Embellishments
10.15 Wrangling Behavior Tags
10.16 Three Ways to Capture Customer Satisfaction
10.17 Extreme Status Tracking for Real-Time Customer Analysis
10.18 Think Globally, Act Locally
10.19 Warehousing without Borders
10.20 Spatially Enabling Your Data Warehouse
10.21 Multinational Dimensional Data Warehouse Considerations
10.22 Industry Standard Data Models Fall Short
10.23 An Insurance Data Warehouse Case Study
10.24 Traveling through Databases
10.25 Human Resources Dimensional Models
10.26 Managing Backlogs Dimensionally
10.27 Not So Fast
10.28 The Budgeting Chain
10.29 Compliance-Enabled Data Warehouses
10.30 Clicking with Your Customer
10.31 The Special Dimensions of the Clickstream
10.32 Fact Tables for Text Document Searching
10.33 Enabling Market Basket Analysis
11 Back Room ETL and Data Quality
11.1 Surrounding the ETL Requirements
11.2 The 34 Subsystems of ETL
11.3 Six Key Decisions for ETL Architectures
11.4 Three ETL Compromises to Avoid
11.5 Doing the Work at Extract Time
11.6 Is Data Staging Relational?
11.7 Staging Areas and ETL Tools
11.8 Should You Use an ETL Tool?
11.9 Call to Action for ETL Tool Providers
11.10 Document the ETL System
11.11 Measure Twice, Cut Once
11.12 Brace for Incoming
11.13 Building a Change Data Capture System
11.14 Disruptive ETL Changes
11.15 New Directions for ETL
11.16 Dealing With Data Quality: Don't Just Sit There, Do Something!
11.17 Data Warehouse Testing Recommendations
11.18 Dealing with Dirty Data
11.19 An Architecture for Data Quality
11.20 Indicators of Quality: The Audit Dimension
11.21 Adding an Audit Dimension to Track Lineage and Confidence
11.22 Add Uncertainty to Your Fact Table
11.23 Have You Built Your Audit Dimension Yet?
11.24 Is Your Data Correct?
11.25 Eight Recommendations for International Data Quality
11.26 Using Regular Expressions for Data Cleaning
11.27 Pipelining Your Surrogates
11.28 Unclogging the Fact Table Surrogate Key Pipeline
11.29 Replicating Dimensions Correctly
11.30 Identify Dimension Changes Using Cyclic Redundancy Checksums
11.31 Maintaining Back Pointers to Operational Sources
11.32 Creating Historical Dimension Rows
11.33 Facing the Re-Keying Crisis
11.34 Backward in Time
11.35 Early-Arriving Facts
11.36 Slowly Changing Entities
11.37 Using the SQL MERGE Statement for Slowly Changing Dimensions
11.38 Creating and Managing Shrunken Dimensions
11.39 Creating and Managing Mini-Dimensions
11.40 Creating, Using, and Maintaining Junk Dimensions
11.41 Building Bridges
11.42 Being Offline as Little as Possible
11.43 Working in Web Time
11.44 Real-Time Partitions
11.45 The Real-Time Triage

12 Technical Architecture Considerations

12.1 Can the Data Warehouse Benefit from SOA?
12.2 Picking the Right Approach to MDM
12.3 Building Custom Tools for the DW/BI System
12.4 Welcoming the Packaged App
12.5 ERP Vendors: Bring Down Those Walls
12.6 Building a Foundation for Smart Applications
12.7 RFID Tags and Smart Dust
12.8 Is Big Data Compatible with the Data Warehouse?
12.9 The Evolving Role of the Enterprise Data Warehouse in the Era of Big Data Analytics
12.10 Newly Emerging Best Practices for Big Data
12.11 The Hyper-Granular Active Archive
12.12 Columnar Databases: Game Changers for DW/BI Deployment
12.13 There Is no Database Magic
12.14 Relating to OLAP
12.15 Dimensional Relational versus OLAP: The Final Deployment Conundrum
12.16 Microsoft SQL Server Comes of Age for Data Warehousing
12.17 The Aggregate Navigator
12.18 Aggregate Navigation with (Almost) No Metadata
12.19 The Second Revolution of User Interfaces
12.20 Designing the User Interface
12.21 Meta Meta Data Data
12.22 Creating the Metadata Strategy
12.23 Leverage Process Metadata for Self-Monitoring DW Operations
12.24 Watching the Watchers
12.25 Catastrophic Failure
12.26 Digital Preservation
12.27 Creating the Advantages of a 64-Bit Server
12.28 Server Confi guration Considerations
12.29 Adjust Your Thinking for SANs

13 Front Room Business Intelligence Applications
13.1 The Promise of Decision Support
13.2 Beyond Paving the Cow Paths
13.3 BI Components for Business Value
13.4 Big Shifts Happening in BI
13.5 Behavior: The Next Marquee Application
13.6 Three Critical Components for Successful Self-Service BI
13.7 Leverage Data Visualization Tools, But Avoid Anarchy
13.8 Think Like a Software Development Manager
13.9 Standard Reports: Basics for Business Users
13.10 Building and Delivering BI Reports
13.11 The BI Portal
13.12 Dashboards Done Right
13.13 Don't Be Overly Reliant on Your Data Access Tool's Metadata
13.14 Making Sense of the Semantic Layer
13.15 Digging into Data Mining   
13.16 Preparing for Data Mining
13.17 The Perfect Handoff
13.18 Get Started with Data Mining Now
13.19 Leverage Your Dimensional Model for Predictive Analytics
13.20 Does Your Organization Need an Analytic Sandbox?
13.21 Simple Drill Across in SQL
13.22 An Excel Macro for Drilling Across
13.23 The Problem with Comparisons
13.24 SQL Roadblocks and Pitfalls
13.25 Features for Query Tools
13.26 Turbocharge Your Query Tools
13.27 Smarter Data Warehouses

14 Maintenance and Growth Considerations
14.1 Don't Forget the Owner's Manual
14.2 Let's Improve Our Operating Procedures
14.3 Marketing the DW / BI System
14.4 Coping with Growing Pains
14.5 Data Warehouse Checkups
14.6 Boosting Business Acceptance
14.7 Educate Management to Sustain DW / BI Success
14.8 Getting Your Data Warehouse Back on Track
14.9 Upgrading Your BI Architecture
14.10 Four Fixes for Legacy Data Warehouses
14.11 A Data Warehousing Fitness Program for Lean Times
14.12 Enjoy the Sunset
15 Final Thoughts
15.1 Final Word of the Day: Collaboration
15.2 Tried and True Concepts for DW / BI Success
15.3 Key Tenets of the Kimball Method
15.4 The Future Is Bright
Article Index

About the Authors
Ralph Kimball
is the Founder of the Kimball Group and a leading visionary in the data warehouse industry.
Margy Ross is President of the Kimball Group and has focused exclusively on DW/BI solutions since 1982.
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