OLAP

Online Analytical Processing

A technology that enables users to analyze large volumes of multidimensional data interactively and efficiently from multiple perspectives. OLAP is a key Business Intelligence (BI) component designed to support complex queries, ad-hoc analysis, and decision-making. Key characteristics of OLAP include:

• Multidimensional data: OLAP systems organize data into multidimensional structures called cubes, which allow users to analyze data along multiple dimensions (e.g., time, geography, product, and sales channel).
• Aggregation: OLAP systems pre-calculate and store aggregated data (e.g., sums, averages, and counts) at various levels of detail to provide fast query response times.
• Drill-down and roll-up: Users can drill down into the data to view more detailed information or roll up to see summarized data views.
• Slice and dice: OLAP allows users to slice the data cube along a specific dimension (e.g., time) and dice it by selecting specific values from multiple dimensions (e.g., a particular product in a specific region).
• Pivot and rotate: Users can pivot the data cube to change the perspective of the analysis and rotate the cube to swap the dimensions.

OLAP systems are typically built on top of data warehouses, which store historical and aggregated data from various sources. There are two main types of OLAP systems:

• Multidimensional OLAP (MOLAP): This type of OLAP system stores data in proprietary multidimensional databases, providing fast query performance but requiring more storage space.
• Relational OLAP (ROLAP): This type of OLAP system stores data in relational databases and generates SQL queries to calculate the aggregations on the fly, offering more flexibility but potentially slower query performance.

OLAP is used in various business scenarios, such as sales analysis, financial reporting, budgeting and forecasting, and customer behavior analysis. By providing a powerful and interactive way to explore and analyze data, OLAP helps organizations gain valuable insights, identify trends and patterns, and make data-driven decisions to improve business performance.

• Abbreviation: OLAP