Do We Need Data Normalization Anymore?

This post was originally published on DZone (IoT)

Many different roles in the technology world come into contact with data normalization as a routine part of many projects. Developers, database administrators, domain modelers, business stakeholders, and many more progress through the normalization process just as they would breathing. And yet, can something that seems so integral become obsolete?

As the database landscape becomes more diverse and hardware becomes more powerful, we might wonder if the practice of data normalization is required anymore. Should we be fretting over optimizing data storage and querying so that we return the minimum amount of data? Or if we should, do certain data structures make it more vital to solve those problems than others?

In this article, we will review the process of data normalization and evaluate when this is needed, or if it is still a necessary part of digitally storing and retrieving data.

What Is Data Normalization?

Data normalization is optimizing data structures in a relational database to ensure data integrity and query efficiency. It reduces redundancy and improves accuracy by putting the data through a series of steps to normalize the structure (normal forms). At its core, data normalization helps avoid insert, update, and delete data anomalies. These anomalies occur when

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