Data Modelers Still Have Jobs: Adjusting for the NoSQL Environment
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Data modeling emerged in the 1970’s in response to the needs of database designers. This accident of history has influenced perceptions and practices of data modeling in harmful ways. Most notably, ...
Data modeling emerged in the 1970’s in response to the needs of database designers. This accident of history has influenced perceptions and practices of data modeling in harmful ways. Most notably, business-focused requirements analysis has been wrongly commingled with relational modeling. Compounding the problem, vendors have produced data-modeling tools that blur the important distinction between the client’s problem and the technologist’s solution.
Enter NoSQL, with its promise of liberating practitioners from the tiresome burden of designing relational databases. The chance to dispense with relational modeling was embraced enthusiastically, but for many organizations, it has meant discarding the only rigorous activity that had any hope of formally expressing the client’s data needs. This is a textbook case of throwing out the baby with the bathwater. This presentation shows you how to save the baby, and your career as a data modeler.
Understanding the client’s data problem remains essential, regardless of the technology used to build the solution. For that matter, understanding the client’s data problem is the first step toward making an informed choice of technology for the solution.
Using concrete, real-world examples, the presenter will show the following:
- How abandoning modeling altogether is a recipe for disaster, even in—or especially in—NoSQL environments
How experienced relational modelers can leverage their skills for NoSQL projects
- How the NoSQL context both simplifies and complicates the modeling endeavor
- How lessons learned modeling for NoSQL projects can make you a more effective modeler for any kind of project
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