Agile Data Warehousing for the Enterprise: A Guide for Solution Architects and Project Leaders
- 15h 21m
- Ralph Hughes
- Elsevier Science and Technology Books, Inc.
- 2016
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 management 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.
- Learn how to quickly define scope and architecture before programming starts
- Includes techniques of process and data engineering that enable iterative and incremental delivery
- Demonstrates how to plan and execute quality assurance plans and includes a guide to continuous integration and automated regression testing
- Presents program management strategies for coordinating multiple agile data mart projects so that over time an enterprise data warehouse emerges
- Use the provided 120-day road map to establish a robust, agile data warehousing program
In this Book
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Abbreviations
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Foreword
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Solving Enterprise Data Warehousing's “Fundamental Problem”
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Primer on Agile Development Methods
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Introduction to Alternative Iterative Methods
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Part I References
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Essential DW/BI Background and Definitions
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Recap of Agile DW/BI Coding Practices
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Eliminating Risk Through Nested Iterations
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Part II References
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Balancing between Two Extremes
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Redefining the Epic Stack to Enable Value Accounting
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Artifacts for the Generic Requirements Value Chain
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Artifacts for the Enterprise Requirements Value Chain
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Intersecting Value Chains for a Stereoscopic Project Definition
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Part III References
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Traditional Data Modeling Paradigms and Their Discontents
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Surface Solutions Using Data Virtualization and Big Data
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Agile Integration Layers with Hyper Normalization
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Fully Agile EDW with Hyper Generalization
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Part IV References
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Why We Test and What Tests to Run
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Designating Who, When, and Where
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Deciding How to Execute the Test Cases
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Part V References
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The Agile EDW Subrelease Cycle
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Part VI References
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