Breaking down our data for vertical partitioning

While Chapter 5, Moving From a Relational Mindset, briefly touched on single-table design and design patterns for breaking down data to enhance efficiency and scalability, this section aims to further expand on those concepts within the context of vertical partitioning. Our learning begins by reviewing the advantages of breaking down data and how this approach can prove advantageous in many scenarios.

Advantages of breaking down data for vertical partitioning

Breaking down large JSON structures into smaller chunks, forming part of the same item collection or collections meant to be retrieved together, offers a significant advantage – highly efficient data retrieval. This approach minimizes the need for multiple network requests when accessing related data. However, it is crucial to note that this breakdown might involve denormalization and data duplication, potentially requiring multiple writes from the application.

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