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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 ...
This is a comparison of object–relational database management systems (ORDBMSs). Each system has at least some features of an object–relational database ; they vary widely in their completeness and the approaches taken.
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.
The term OLAP was created as a slight modification of the traditional database term online transaction processing (OLTP). [2] OLAP is part of the broader category of business intelligence, which also encompasses relational databases, report writing and data mining. [3]
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.
A data stream management system (DSMS) is a computer software system to manage continuous data streams. It is similar to a database management system (DBMS), which is, however, designed for static data in conventional databases. A DBMS also offers a flexible query processing so that the information needed can be expressed using queries.
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]
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.