-
Book Overview & Buying
-
Table Of Contents
AI-Ready PostgreSQL 18
By :
This chapter provided a high-level overview of the business requirements for analytical solutions, their architectural implications, and how to meet them with PostgreSQL. We learned that analytics architectures optimize data for read-oriented, quick retrieval and analysis of large datasets, potentially resulting from multiple source systems. Also, they contain historical data to support trend analysis and often combine classical numbers-oriented data with text-based documents.
In the following chapters, we will dive into the denormalized analytics data model, exploring data vaults and star schemas, before looking at how PostgreSQL can be used to transform transactional, normalized data into denormalized, analytics-focused models.
The denormalized models provide the foundation for analytics queries. We will show you how to create groups, use aggregates, and compile data using cubes, roll-ups, window functions, and CTEs. Last, but not least, we will explore PostgreSQL...