Agile Data Warehousing for the Enterprise A Guide for Solutions Architects and Project Leaders 1st Edition by Ralph Hughes ISBN 9780123964649 0123964644

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ISBN 10: 0123964644
ISBN 13: 9780123964649
Author: Ralph Hughes

Building upon his earlier book that detailed agile data warehousing programming techniques for the Scrum master, Ralph’s latest work illustrates the agile interpretations of the remaining software engineering disciplines:

• Requirements managements benefits from streamlined templates that not only define projects quickly, but ensure nothing essential is overlooked.
• Data engineering receives two new “hyper modeling” techniques, yielding data warehouses that can be easily adapted when requirements change without having to invest in ruinously expensive data-conversion programs. 
• Quality assurance advances with not only a stereoscopic top-down and bottom-up planning method, but also the incorporation of the latest in automated test engines. 

Use this step-by-step guide to deepen your own application development skills through self-study, show your teammates the world’s fastest and most reliable techniques for creating business intelligence systems, or ensure that the IT department working for you is building your next decision support system the right way.

Agile Data Warehousing for the Enterprise A Guide for Solutions Architects and Project Leaders 1st Edition Table of contents:

Chapter 1. Solving Enterprise Data Warehousing’s “Fundamental Problem”

The Agile Solution in a Nutshell

Five Legs to Stand Upon

The Agile EDW Alternative is Ready to Deploy

Defining a Baseline Method for Agile EDW

Plenty of Motivation to “Go Agile”

Structure of the Presentation Ahead

Summary

Part I: Summaries of Generic Agile Development Methods

Chapter 2. Primer on Agile Development Methods

Defining “Agile”

Agile Manifesto Values and Principles

Scrum in a Nutshell

Contributions from Extreme Programming

Summary

Chapter 3. Introduction to Alternative Iterative Methods

Lean Software Development

Kanban

The Hybrid “Scrumban” Approach

Rational Unified Process

Summary

Part I References

Chapter 2

Part II: Review of Fast EDW Coding and Risk Mitigation

Chapter 4. Essential DW/BI Background and Definitions

Primary Source for DW/BI Standards

Basic Business Terms

Data and Information Terms

Information Services Terms

Software Engineering Terms

Basic Architectural Concepts

Architectural Frameworks

Additional Data Warehousing Concepts

Traditional Project Management Terms

Summary

Chapter 5. Recap of Agile DW/BI Coding Practices

Iterative Coding Alone Significantly Improves BI Projects

New Roles for DW/BI Projects

80/20 Specifications

Developer Stories

Current Estimates

Adding Techniques from Kanban

Evidence-Based Service Level Agreements

Proof that Agile DW/BI Works

Summary

Chapter 6. Eliminating Risk Through Nested Iterations

EDW Programs Slip into “231 Swamps”

Agile’s Fundamental Risk Mitigation Technique

Agile Edw’s Extended Risk Mitigation Techniques

Summary

Part II References

Chapter 4

Part III: Agile EDW Requirements Management

Chapter 7. Balancing between Two Extremes

Building the Case for Effective Requirements Management

Easy to Overinvest in Requirements Management

Reasons Not to Overinvest in Requirement Work

Agile’s Approach Centers on Balance

Two Intersecting Requirements Management Value Chains

Business Analysts Implicit in Two Project Lead Roles

Summary

Chapter 8. Redefining the Epic Stack to Enable Value Accounting

Toward a Robust Epic Decomposition Framework

Testing Whether Stories are Good Enough

Clarifying Everything with Value Accounting

Allocating Value Throughout an Epic Tree

Value Buildups by Environment Provide Motivation and Clarity

Summary

Chapter 9. Artifacts for the Generic Requirements Value Chain

Beware of Requirements Churn

User Modeling/Personas

End Users’ Hierarchy of Needs

Mind Maps and Fishbone Diagrams

Vision Boxes

Vision Statements

Product Roadmaps

Summary

Chapter 10. Artifacts for the Enterprise Requirements Value Chain

The Generic Value Chain Can Overlook Crucial Requirements

ERM as a Flexible RM Approach

Focusing on Enterprise Aspects of Project Requirements

Uncovering Project Goals with Sponsor’s Concept Briefing

Identifying Project Objectives with Stakeholder’s Requests

Sketching the Solution with a Vision Document

Segmenting the Project with Subrelease Overview

Providing Developer Guidance with Module Use Cases

Summary

Chapter 11. Intersecting Value Chains for a Stereoscopic Project Definition

Intersecting the Two Value Chains

Addressing Nonfunctional Requirements

Supporting the Organization’s Software Release Cycle

Techniques for the Elaboration Phase

Prioritizing Project Backlogs

Managing Incremental Precision

Effort Levels by Team Roles

Conquering Complex Business Rules with an Embedded Method

Interfacing with Project Governance

Not Returning to a Waterfall Approach

Summary

Part III References

Chapter 7

Part IV: Agile EDW Data Engineering

Chapter 12. Traditional Data Modeling Paradigms and Their Discontents

EDW at a Crossroads

Models, Architectures, and Paradigms

Normalization Basics

Generalization Basics

The Standard Approach and its Data Modeling Paradigms

The Traditional Integration Layer as a Challenged Concept

“Straight-To-Star” as a Controversial Alternative

Four Change Cases for Appraising a Data Modeling Paradigm

Summary

Chapter 13. Surface Solutions Using Data Virtualization and Big Data

Leveraging Shadow it

Faster Value Delivery with Data Virtualization

An Agile Role for Big Data

Summary

Chapter 14. Agile Integration Layers with Hyper Normalization

Hyper Normalization Hinges on “Ensemble Modeling”

Hyper Normalized Data Modeling Concepts

Reusable ETL Modules Accelerate New Development

Common Data Retrieval Challenges and Their Solutions

Re-Architecting the EDW for Hyper Normalization

Enabling Evolution of Existing EDW Components

HNF-Powered Agile Solutions

Evidence of Success

Summary

Chapter 15. Fully Agile EDW with Hyper Generalization

Hyper Generalization Involves a Mix of Modeling Strategies

HGF Enables Model-Driven Development and Fast Deliveries

Loading Data into the Hyper Generalized Integration Layer

Retrieving Information from a Hyper Generalized EDW

Model-Driven Evolution and Fast Adaptation

Supporting Derived Elements

Addressing Performance Concerns

Demonstrating Agility Through Four Change Cases

HGF-Powered Agile Solutions

Evidence of Success

Summary

Part IV References

Chapter 12

Part V: Agile EDW Quality Management Planning

Chapter 16. Why We Test and What Tests to Run

Why Test?

An Agile Approach to Quality Assurance

“What to Test?” Answered with Top-Down Planning

A 2×2 Planning Matrix for Top-Down Test Selection

“What to Test?” Answered Bottom-Up

Summary

Chapter 17. Designating Who, When, and Where

Who Shall Write the Tests?

When Should Teammates Perform Their QA Duties?

Where Should Teammates Perform Their QA Duties?

Key Quality Responsibilities by Team Role

The Overarching Duties of the System Tester

How Many Testers are Needed?

Summary

Chapter 18. Deciding How to Execute the Test Cases

Good Agile Quality Plans Involve Numerous Test Executions

Step 1: Update the Top-Down Plan

Step 2: Start Building the Parameter-Driven Widgets

Step 3: Plan Out the Test Data Sets

Step 4: Implement the Engine, Whether Manual or Automated

Step 5: Define the Project’s Set of Testing Aspects

Step 6: Build and Populate the Test Data Repository

Step 7: Quantify the Testing Objectives

Step 8: Begin Creating Test Cases

Step 9: Start Up the Engine

Step 10: Visualize Project Progress with Quality Assurance

Step 11: Document the Team’s Success

Summary

Part V References

Chapter 16

Part VI: Integrating the Pieces of the Agile EDW Method

Chapter 19. The Agile EDW Subrelease Cycle

Making the Release Cycle a Repeatable Process

Traditional Notions of Data Governance

The Agile EDW Subrelease Value Cycle

Centering the Value Cycle on Data Governance and Quality

Guiding the Agile EDW Transition

Summary

Part VI References

Chapter 19

Index

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Tags: Ralph Hughes, Agile Data Warehousing, Enterprise, Solutions Architects, Project Leaders