OLAP: Overview and Queries

Description

This lecture covers the basics of On-line Analytical Processing (OLAP), including data warehouses, data lakes, multidimensional data models, and OLAP queries. It explains the concepts of star and snowflake schemas, dimension hierarchies, and the CUBE operator. The instructor discusses OLAP queries, such as aggregations, group by operations, and slicing and dicing. Examples of OLAP queries, roll-up, drill-down, and pivoting are provided. The lecture also explores the use of SQL for OLAP queries, optimizations like bitmap indexes, join indexes, and star joins, and the computation of super-aggregates in data cubes.

Official source

https://mediaspace.epfl.ch/media/0_ve8w8orc

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An OLAP cube is a multi-dimensional array of data. Online analytical processing (OLAP) is a computer-based technique of analyzing data to look for insights. The term cube here refers to a multi-dimensional dataset, which is also sometimes called a hypercube if the number of dimensions is greater than three. A cube can be considered a multi-dimensional generalization of a two- or three-dimensional spreadsheet. For example, a company might wish to summarize financial data by product, by time-period, and by city to compare actual and budget expenses.

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