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Data processing, management and performance related features OLAP server Real Time Write-back Partitioning Usage Based Optimizations Load Balancing and Clustering Apache Doris Yes Yes Yes Yes Yes Apache Druid: Yes ? Yes Yes Yes Apache Kylin: Yes [34] No Yes Yes Yes Apache Pinot: Yes Yes Yes Yes Yes ClickHouse: Yes Yes Yes Yes Yes Essbase: Yes ...
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]
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.
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 ...
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.
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. [7] [6] As a result of the complexity in the information architecture and infrastructure of both OLTP and OLAP systems, data analysis is delayed.
Streaming algorithms have several applications in networking such as monitoring network links for elephant flows, counting the number of distinct flows, estimating the distribution of flow sizes, and so on. [9] They also have applications in databases, such as estimating the size of a join [citation needed].