Analytics Engineering with SQL and dbt

Chapter 2. Data Modeling for Analytics

In today’s data-driven world, organizations rely more and more on data analytics to gain valuable insights and make informed decisions. Data modeling plays an imperative role in this process, providing a solid foundation for structuring and organizing data to support effective analysis. In addition, understanding the concepts of data modeling and normalization is essential to realizing the full potential of analytics and gaining actionable insights from complex datasets.

Data modeling is about defining the structure, relationships, and attributes of data entities within a system. An essential aspect of data modeling is the normalization of the data. Data normalization is a technique for eliminating data redundancy and improving data integrity. It involves breaking data into logical units and organizing them into separate tables, which reduces data duplication and improves overall database efficiency. Normalization ensures that data is stored in a structured and consistent manner, which is critical for accurate analysis and reliable results.

Regarding analytics, data modeling provides a solid foundation for creating analytical models. Analysts can design effective models that capture relevant information and support the desired analytics objectives by understanding the relationships among entities and data structures. In other words, a well-designed data model enables analysts to perform complex queries, join tables, and aggregate data to ...