By Ken W. Collier
Utilizing Agile tools, you could deliver a ways better innovation, price, and caliber to any info warehousing (DW), company intelligence (BI), or analytics undertaking. in spite of the fact that, traditional Agile equipment needs to be conscientiously tailored to deal with the original features of DW/BI tasks. In Agile Analytics, Agile pioneer Ken Collier exhibits tips on how to do exactly that.
Collier introduces platform-agnostic Agile options for integrating infrastructures including various operational, legacy, and uniqueness structures that blend advertisement and customized code. utilizing operating examples, he exhibits the right way to deal with analytics improvement groups with generally diversified ability units and the way to aid huge, immense and fast-growing facts volumes. Collier’s options provide optimum price no matter if your initiatives contain “back-end” facts administration, “front-end” company research, or both.
Part I specializes in Agile undertaking administration suggestions and supply staff coordination, introducing middle practices that form the best way your Agile DW/BI venture neighborhood can collaborate towards success
Part II offers technical equipment for allowing non-stop supply of industrial worth at production-quality degrees, together with evolving greater designs; test-driven DW improvement; model keep an eye on; and undertaking automation
Collier brings jointly confirmed suggestions you could observe correct now—whether you’re an IT decision-maker, info warehouse expert, database administrator, enterprise intelligence expert, or database developer. together with his support, you could mitigate undertaking probability, increase company alignment, in achieving larger results—and have a good time alongside the way in which.
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Extra resources for Agile Analytics: A Value-Driven Approach to Business Intelligence and Data Warehousing (Agile Software Development Series)
Design and implementation of data models are required for both the integration and presentation repositories. Relational data models are distinctly different from dimensional data models, and each has unique properties. Moreover, relational data modelers may not have dimensional modeling expertise and vice versa. ETL development. ETL refers to the extraction of data from source systems into staging, the transformations necessary to recast source data for analysis, and the loading of transformed data into the presentation repository.
I will demonstrate how you can apply these on your projects, and how you can tailor them to the nuances of your environment. ” so I should probably take you a bit further than the mountaineering analogy. Here’s What Agile Analytics Is So here is a summary of the key characteristics of Agile Analytics. This is simply a high-level glimpse at the key project traits that are the mark of agility, not an exhaustive list of practices. Throughout the remainder of this book I will introduce you to a set of specific practices that will enable you to achieve agility on your DW/BI projects.
This chapter outlines the tenets of Agile Analytics and establishes the foundational principles behind each of the practices and techniques that are introduced in the successive chapters in this book. Agile is a reserved word when used to describe a development style. It means something very specific. Unfortunately, “agile” occasionally gets misused as a moniker for processes that are ad hoc, slipshod, and lacking in discipline. Agile relies on discipline and rigor; however, it is not a heavyweight or highly ceremonious process despite the attempts of some methodologists to codify it with those trappings.