OLAP and data warehousing are both used in the context of specific database-design patterns, where a database is used for analysis and reporting. In contrast to OLTP, OLAP deals with bigger amounts of data and a smaller number of concurrent sessions and transactions, but the amount of changes within a transaction usually is bigger. The database structure is often denormalized to improve query performance. A database that is a part of an OLAP solution is often called a data warehouse.
In this chapter, we discussed structuring data in a data warehouse, how to load data there, and how to optimize the database performance by applying partitioning, and using parallel query execution and index-only scans.
In the next chapter, we will discuss the extended data types supported by PostgreSQL, such as arrays and JSON. These data types make it possible to implement complicated business...
