By Ken W. Collier
Utilizing Agile equipment, you could convey a long way larger innovation, worth, and caliber to any info warehousing (DW), company intelligence (BI), or analytics venture. even if, traditional Agile tools needs to be conscientiously tailored to handle the original features of DW/BI tasks. In Agile Analytics, Agile pioneer Ken Collier exhibits the way to do exactly that.
Collier introduces platform-agnostic Agile suggestions for integrating infrastructures together with various operational, legacy, and uniqueness structures that blend advertisement and customized code. utilizing operating examples, he indicates easy methods to deal with analytics improvement groups with generally diversified ability units and the way to help huge, immense and fast-growing info volumes. Collier’s recommendations provide optimum price even if your initiatives contain “back-end” facts administration, “front-end” enterprise research, or both.
Part I makes a speciality of Agile undertaking administration options and supply workforce coordination, introducing center practices that form the way in which your Agile DW/BI undertaking group can collaborate towards success
Part II provides technical tools for permitting non-stop supply of commercial price at production-quality degrees, together with evolving enhanced designs; test-driven DW improvement; model keep watch over; and venture automation
Collier brings jointly confirmed strategies you could observe correct now—whether you’re an IT decision-maker, information warehouse specialist, database administrator, enterprise intelligence expert, or database developer. together with his support, you could mitigate undertaking threat, increase enterprise alignment, in achieving higher results—and have a good time alongside the best way.
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Extra resources for Agile Analytics: A Value-Driven Approach to Business Intelligence and Data Warehousing (Agile Software Development Series)
Production automation. Data warehouses are generally designed for periodic automated updates when new and modified data is slurped into the warehouse so that users can view the most recent data available. These automated update processes must have built-in fail-over strategies and must ensure data consistency and correctness. General systems and database administration. Data warehouse developers must have many of the same skills held by the typical network administrator and database administrator.
Through this work we have come to value: Individuals and interactions over processes and tools Working DW/BI systems over comprehensive documentation End-user and stakeholder collaboration over contract negotiation Responding to change over following a plan That is, while there is value in the items on the right, we value the items on the left more. 2. org WHAT IS AGILE A NALYTICS ? 7 I didn’t want to mess with the original manifesto too much, but it is important to acknowledge that DW/BI systems are fundamentally different from application software.
Performance is unacceptable. Availability of the warehouse applications is poor. There is no ability to expand. The data and/or reports are poor. The project is not cost-justified. Management does not recognize the benefits of the project. In other words, simply completing the technical implementation of a data warehouse doesn’t constitute success. Take another look at this list. Nearly every situation is “customer”-focused; that is, primarily end users determine whether a project is successful.