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REST API; Apache Doris No No No No Yes [20] Yes No Superset, Redash, Metabase, Tableau, Qlik, Pivot, PowerBI Yes Yes Apache Druid: No No No No Yes Druid SQL No Superset, Pivot, Redash Yes Yes Apache Kylin: Yes No Yes No Yes Yes Superset, Zeppelin, Tableau, Qlik, Redash, Microsoft Excel Yes Yes Apache Pinot: No No No No Yes Yes No Superset ...
A good example of metadata is the cataloging system found in libraries, which records for example the author, title, subject, and location on the shelf of a resource. Another is software system knowledge extraction of software objects such as data flows, control flows, call maps, architectures, business rules, business terms, and database schemas.
For example, all sales offices are rolled up to the sales department or sales division to anticipate sales trends. By contrast, the drill-down is a technique that allows users to navigate through the details. For instance, users can view the sales by individual products that make up a region's sales.
Metadata This file contains additional information, probably added from the digital camera or scanner used to create or digitize it. If the file has been modified from its original state, some details may not fully reflect the modified file.
Short title: example derived form Ghostscript examples: Image title: derivative of Ghostscript examples "text_graphic_image.pdf", "alphabet.ps" and "waterfal.ps"
A metadata registry typically has the following characteristics: Protected environment where only authorized individuals may make changes; Stores data elements that include both semantics and representations; Semantic areas of a metadata registry contain the meaning of a data element with precise definitions
Microsoft SQL Server Analysis Services (SSAS [1]) is an online analytical processing (OLAP) and data mining tool in Microsoft SQL Server.SSAS is used as a tool by organizations to analyze and make sense of information possibly spread out across multiple databases, or in disparate tables or files.
In metadata, metadata discovery (also metadata harvesting) is the process of using automated tools to discover the semantics of a data element in data sets. This process usually ends with a set of mappings between the data source elements and a centralized metadata registry. Metadata discovery is also known as metadata scanning.