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Multidimensional Expressions (MDX) is a query language for online analytical processing (OLAP) using a database management system. Much like SQL, it is a query language for OLAP cubes. [1] It is also a calculation language, with syntax similar to spreadsheet formulae.
In computing, online analytical processing, or OLAP (/ ˈ oʊ l æ p /), is an approach to quickly answer multi-dimensional analytical (MDA) queries. [1] The term OLAP was created as a slight modification of the traditional database term online transaction processing (OLTP). [2]
OLAP server Authentication Network encryption On-the-Fly [a] Data access Cell security Dimension security Visual totals Apache Doris Built-in, LDAP, Kerberos SSL: Yes Yes Yes Yes Apache Druid: Druid Database authentication SSL: Yes No Yes No Apache Kylin: LDAP, SAML, Kerboros, Microsoft Active Directory SSL Yes No No ? Apache Pinot: HTTP basic ...
Released in 2016 to analyze data that is updated in real time CrateDB: Java C-Store: C++ The last release of the original code was in 2006; Vertica a commercial fork, lives on. DuckDB: C++ An embeddable, in-process, column-oriented SQL OLAP RDBMS Databend Rust An elastic and reliable Serverless Data Warehouse InfluxDB: Rust Time series database
SAP Business Warehouse (SAP BW) is SAP’s Enterprise Data Warehouse product. [1] It can transform and consolidate business information from virtually any source system. [citation needed] It ran on industry standard RDBMS until version 7.3 at which point it began to transition onto SAP's HANA in-memory DBMS, particularly with the release of version 7.4.
An OLAP cube is a multi-dimensional array of data. [1] Online analytical processing (OLAP) [ 2 ] 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.
The choice of data orientation is a trade-off and an architectural decision in databases, query engines, and numerical simulations. [1] As a result of these tradeoffs, row-oriented formats are more commonly used in Online transaction processing (OLTP) and column-oriented formats are more commonly used in Online analytical processing (OLAP). [2]
This can enable performance improvements for OLAP queries on large datasets and allows greater vertical compression of similar types of data in a single column. If the read times for column-stored data is fast enough, consolidated views of the data can be performed on the fly , removing the need for maintaining aggregate views and its ...