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Online Analytical Processing (OLAP)

Online Analytical Processing (OLAP) is an approach to working with typically multidimensional data for analytics use cases.

What is Online Analytical Processing?

Online Analytical Processing (OLAP) refers to a category of software that is focused on various requirements for working with data that is used for analytics. This type of data has unique requirements for performance and various tradeoffs are made compared to more standard relational databases.

What is an OLAP database?

OLAP databases are databases designed to store data in multidimensional rows and columns. OLAP databases are designed to be very efficient at reporting and querying analytical data.

What is an OLAP Cube?

An OLAP cube is a multidimensional model that stores information about a particular topic. The cube usually contains data about a hierarchy of different categories, such as sales data for different products. OLAP cubes are used for analyzing and making decisions based on data.

An example use case for an OLAP cube would be for determining the best marketing strategy for a company. A company would break down their various marketing channels into categories, such as television and radio, then determine the effectiveness of each channel by comparing the number of people they reached with the advertising they spent on that channel.


OLAP software is designed to process large volumes of data and analyze the data in aggregate to provide insights. OLAP databases are typically used in business intelligence and data warehousing. OLAP systems are optimized for handling analytic queries that use aggregations, such as calculating the total amount of sales for a given time period.

OLTP stands for Online Transaction Processing. OLTP databases are typically used in applications such as customer relationship management and enterprise resource planning. OLTP systems are optimized for handling transactional queries that update individual records.


What are materialized views?

OLAP databases are designed to help users interact with data that is too big to process in real time. They work by storing data in a multi-dimensional data store and performing calculations on this data store to generate results. In order to maintain fast response times, materialized views can be created on top of OLAP databases. Materialized views are pre-calculated aggregate values which are stored in a separate table, and these tables can be used to answer ad-hoc questions about the data in the underlying table. A downside of this approach is that queries can return stale results depending on how long ago the materialized view was created.

What is star schema?

Star schema is a type of data warehouse schema that is used for dimensional analysis and Online Analytical Processing (OLAP).

The star schema is a hierarchical data model in which tables are organized in a tree structure. The central table, called the fact table, has cross-referencing relationships with the other tables. The other tables in the star schema are called dimension tables. These dimension tables provide the context for the facts in the central table.

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