This blog post was authored by Todd Schraml.
Why Are Logical Data Models So Important?
The logical data model is the foundational artifact expressing the “what” of a development effort. By sharing this logical data model with the project team and business stakeholders, everyone will have a coherent perspective on the data-aspects of the goal in focus. In an implementation neutral fashion, the logical data model educates new team members about the business objects in focus. Designers and engineers that spend time examining the logical model can use their experience to visualize how the final solution will work. In an ideal circumstance, the logical data model exposes transformations across expected states leaving little to the imagination. The defined objects as well as the relationships defined between each entity are the same for everyone. And since these interrelationships are based on the business meanings, the semantics at hand, they are a stable foundation underpinning any chosen physical implementation. There should be no reason for two different people to understand the data model in differing ways.
Upon viewing a logical data model, an experienced developer can think through how a final product will function. Applying that functional visualization onto the data model allows the engineer to mentally step through what things may or may not work well. Within a well-defined logical data model, there is an expected relationship between the arrangement of entities and what kinds of transformations are rational and easily performed. By expressing the proper business rules, the usual manipulations may also be more natural across the data structures. Using this kind of foresight, discussions can occur early on to clarify requirements that may have been somewhat murky and further refine a data model to be more truly expressive of the proper business rules and requirements. In addition, discussions of these kinds of issues can help the team to build consensus on work to be done. This results in a better data model, a stronger team, and a more stable final product.
Why Do Physical Models Need Logical Models?
As one approaches the implementation of a solution, the logical data model gives way to a physical data model. The physical data model may closely mimic the logical data model, or it may be drastically different. Alternate structural approaches such as multidimensional and data vault may come into play, or there may be valid reasons to denormalize in other fashions across various kinds of platforms. There are many valid reasons for deciding to reshape data formations to fit platform or performance needs. However, none of this kind of rework invalidates the value of the logical data model.
If one is physically implementing a large JSON document structure, one is effectively bringing together into a single structure all of the needed elements for a transaction from across the entities of a potentially large logical design. When coding changes to data values within the JSON document, it is the logical structures in the shadows behind it all that can lead the way in showing which pieces impact others or are impacted by others. These impacts viewed from the logical model will guide where the correct code must be placed within the physical implementation to ensure data integrity across the entirety of the document.
It is the logical relationships found within the logical data model that will shape the code necessary to properly manage the data content across whatever physical configurations exist. Not understanding the data’s actual logical relationships may lead to ineffective or faulty code and later rework. Having good logical and physical data models helps designers and engineers to work better through to complete solutions that function as desired, and likely are more flexible in handling rational future changes.
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About the Author
Todd Schraml has over fifteen years experience in application development and maintenance. This includes eleven years focused on data warehousing initiatives and five years experience in database administration on massively parallel processing database management systems. Positions held include Project Manager, Data Warehouse Architect, Technical Lead, Database Administrator, Business Analyst, Developer, and Teacher.
Todd’s focus is on data analysis and design; implementing databases for both operational applications and data warehouses; using new and emerging technologies; and seeking ways to integrate formalized quality practices into Information Technology arenas.