Online analytical processing, more commonly referred to as OLAP, is an approach to database processing. It was introduced in the 1990s as a way to use data for analytical workloads, in contrast to OLTP, which is used for transactional workloads. OLAP processing is associated with the column-oriented database, a database structure for laying out memory that is also designed for analytical workloads.
To illustrate what is meant by analytical processing on column-oriented data, imagine a company’s data in a table with rows representing customer transactions and columns representing data about each transaction (see below diagram); an OLAP query would deal with one or a few columns across very large numbers of rows. For example, summing the total amount of sales across all transactions over a given period of time would be a typical OLAP process.
OLAP entails running complex queries on large amounts of data aggregated from various sources, including OLTP databases, for the purposes of analytics and business intelligence. Year-over-year financial performance or marketing lead generation trends are examples of suitable OLAP use cases.
OLAP at Molecula
Molecula has developed a database platform that is neither row-oriented nor column-oriented. Molecula’s feature-oriented database, FeatureBase, represents data independently of rows and columns, enabling OLAP workloads to perform faster with less hardware than the traditional columnar database. FeatureBase is particularly useful for ML and AI applications due to the nature of its size, speed, volume, and real-time data capabilities. Because of this and the fact that FeatureBase is built on a machine-native bitmap architecture, FeatureBase processing is referred to as OLMP (online machine processing).
To learn more, see FeatureBase.
Learn More About OLAP
Wikipedia entry: Online analytical processing
Holistics blog: The Rise and Fall of the OLAP Cube
Tech Target: OLAP (online analytical processing) definition
Video by Hussein Nasser: Column vs Row Oriented Databases ExplainedNEXT