Data Modeling 101: OLTP data modeling, design, and normalization for the cloud

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How to create a solid foundation for data modeling of OLTP systems

As you undertake a cloud database migration, a best practice is to perform data modeling as the foundation for well-designed OLTP databases. For standard relational database applications, data modeling that incorporates accepted design paradigms, such as normalization, is essential. This makes mastering basic data modeling techniques and avoiding common pitfalls imperative. This blog outlines a solid foundation for data modeling of online transaction processing (OLTP) systems as they move to the cloud. Other types of databases will be covered in subsequent blogs.

Data modeling is a serious scientific method with many rules and best practices. However, the diagram is merely the starting point for an effective and efficient database design. One must also capture the vast quantity of metadata around the OLTP business requirements that must be reflected. I’m not proposing that business logic be held in the database, but it needs to be documented in the data model even if it will be implemented in application code. No matter where you implement it, you must fully document the business requirements in order to produce an effective final result.

Data modeling helps you right-size cloud migrations for cost savings

The cloud offers infinitely scalable resources – but, at a cost. When you make poor database design choices for OLTP applications deployed to the cloud, your company will pay every month for the resulting inefficiencies. Static overprovisioning or dynamic scaling will run up monthly cloud costs very quickly

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