Agile Data Warehousing for the Enterprise. A Guide for by Ralph Hughes

By Ralph Hughes

Building upon his past e-book that distinctive agile information warehousing programming thoughts for the Scrum grasp, Ralph's newest paintings illustrates the agile interpretations of the rest software program engineering disciplines:

  • Requirements administration advantages from streamlined templates that not just outline initiatives quick, yet determine not anything crucial is overlooked.
  • Data engineering gets new "hyper modeling" strategies, yielding information warehouses that may be simply tailored whilst specifications switch with no need to take a position in ruinously pricey data-conversion courses.
  • Quality coverage advances with not just a stereoscopic top-down and bottom-up making plans process, but additionally the incorporation of the most recent in automatic attempt engines.

Use this step by step advisor to deepen your personal program improvement abilities via self-study, exhibit your teammates the world's quickest and most efficient innovations for developing enterprise intelligence platforms, or make sure that the IT division operating for you is construction your subsequent determination aid method the precise way.

  • Learn the best way to fast outline scope and structure earlier than programming begins
  • Includes options of procedure and information engineering that allow iterative and incremental supply
  • Demonstrates how you can plan and execute caliber coverage plans and features a consultant to non-stop integration and automatic regression trying out
  • Presents application administration options for coordinating a number of agile info mart tasks in order that over the years an company info warehouse emerges
  • Use the supplied 120-day highway map to set up a powerful, agile info warehousing software

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Extra info for Agile Data Warehousing for the Enterprise. A Guide for Solutions Architects and Project Leaders

Example text

A sketch of each of these steps follows. Story Conference Starting at the top of the project backlog, the team discusses at a medium level of detail the requirements implicit in the user stories. The team discusses as many user stories as it thinks it can deliver within the iteration’s time box. ” Story points are described more fully later, but for the moment we can think of them as a top-down tool for estimating the “size” of a user story—that is, the amount of work it will take to deliver. Story points are derived using pairwise comparisons of the requested work to other modules that the team has already built and remembers quite well.

Teams should also be very clear on the full set of responsibilities that an individual takes on when he or she accepts a given responsibility. For example, accepting responsibility to code a task also means taking ownership of confirming the details of requirements, design, and testing, not just rushing to churn out 1,000 lines of code. SUMMARY The iterative development techniques that we will soon see incorporated into agile data warehousing hail from two separate schools of incremental software engineering: those associated with the agile manifesto and those linked to lean software development.

If integration testing is important, then we’ll integrate and test several times a day (continuous integration). If short iterations are good, we’ll make the iterations really, really short—seconds and minutes and hours, not weeks and months and years (the Planning Game). ” XP was formalized soon after Scrum was introduced, and its programming techniques complemented well Scrum’s work-package management patterns. It was no surprise, then, that by the mid-2000s, the two methods were often being taught as one.

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