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A list of OLAP features that are not supported by all vendors. All vendors support features such as parent-child, multilevel hierarchy, drilldown. Data processing, management and performance related features:
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]
The name Druid comes from the shapeshifting Druid class in many role-playing games, to reflect that the architecture of the system can shift to solve different types of data problems. Druid is commonly used in business intelligence-OLAP applications to analyze high volumes of real-time and historical data. [4]
Stream processing is essentially a compromise, driven by a data-centric model that works very well for traditional DSP or GPU-type applications (such as image, video and digital signal processing) but less so for general purpose processing with more randomized data access (such as databases). By sacrificing some flexibility in the model, the ...
The term "transaction" can have two different meanings, both of which might apply: in the realm of computers or database transactions it denotes an atomic change of state, whereas in the realm of business or finance, the term typically denotes an exchange of economic entities (as used by, e.g., Transaction Processing Performance Council or commercial transactions.
Streaming data is data that is continuously generated by different sources. Such data should be processed incrementally using stream processing techniques without having access to all of the data. In addition, it should be considered that concept drift may happen in the data which means that the properties of the stream may change over time.
Online analytical processing (OLAP) covers the analytical processing involved in creating, synthesizing, and managing data. With greater data demands among businesses, [citation needed] OLAP also has evolved. To meet the needs of applications, both technologies are dependent on their own systems and distinct architectures.
Data independence is of particularly high value for analytics. Data need no longer reside in spreadsheets. Instead the functional database acts as a central information resource. The spreadsheet acts as a user interface to the database, so the same data can be shared by multiple spreadsheets and multiple users.