Modeling considerations for analytics, AI, and ML
As with relational transactional applications, analytics applications require data to be modeled, stored, and accessed to address the application’s design aspects. While the business, functional, technical, and regulatory requirements vary for each application, there are some fundamental operational and design needs that are generally considered the baseline for all analytical data modeling. We’ll look at a few of them in this section:
- Understand the analytical requirements: Before diving into data modeling, it’s important to have a clear understanding of your analytical requirements. Define the specific questions you want to answer or the insights you want to derive from your data. This understanding will guide your data modeling efforts and help you design a database structure that aligns with your analytical goals.
- Denormalize your data: Normalization is a widely adopted practice in traditional database...
