Data Modeling 201 for the cloud: designing databases for data warehouses

*** This blog is based upon webcast which can be watched here. ***

Designing databases for data warehouses or data marts is intrinsically much different than designing for traditional OLTP systems. In fact, many commonly accepted best practices for designing OLTP databases could well be considered worst practices for these purely analytical systems. Accordingly, data modelers must embrace some new tricks when designing data warehouses and data marts. And although some of the advice contained within may seem contrary to what you are comfortable with, please keep an open mind. Remember that Seinfeld’s George Costanza did not land his dream job with the NY Yankees until he embraced doing the opposite of every idea he had, as seen in the video clip below. So, let go of any old OLTP design.

Data modeling for the cloud: good database design means “right size” and savings

As with the part 1 of this blog series, the cloud is not nirvana. Yes, it offers essentially infinitely scalable resources. But you must pay for using them. When you make poor database design choices for applications deployed to the cloud, then your company gets to pay every month for all inevitably resulting inefficiencies. Static over-provisioning or dynamic scaling will run up monthly cloud costs very quickly on a bad design. So, you really should get familiar with your cloud providers sizing vs. cost calculator.

Look at Figure 1 below. It shows pricing for a data warehousing project with just 4 TBs of data,

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