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The snowflake schema is in the same family as the star schema logical model. In fact, the star schema is considered a special case of the snowflake schema. The snowflake schema provides some advantages over the star schema in certain situations, including: Some OLAP multidimensional database modeling tools are optimized for snowflake schemas. [3]
A database shard, or simply a shard, is a horizontal partition of data in a database or search engine. Each shard may be held on a separate database server instance, to spread load. Some data within a database remains present in all shards, [ a ] but some appear only in a single shard.
A partition is a division of a logical database or its constituent elements into distinct independent parts. Database partitioning refers to intentionally breaking a large database into smaller ones for scalability purposes, distinct from network partitions which are a type of network fault between nodes. [ 1 ]
Snowflake Inc. is an American cloud-based data storage company. Headquartered in Bozeman, Montana, it operates a platform that allows for data analysis and simultaneous access of data sets with minimal latency. [1] It operates on Amazon Web Services, Microsoft Azure, and Google Cloud Platform.
Time taken to access any record is the same because the same number of nodes is searched; Index is a full index so data file does not have to be ordered; Pros and cons versatile data structure – sequential as well as random access; access is fast; supports exact, range, part key and pattern matches efficiently.
The inverted file data model can put indexes in a set of files next to existing flat database files, in order to efficiently directly access needed records in these files. Notable for using this data model is the ADABAS DBMS of Software AG, introduced in 1970. ADABAS has gained considerable customer base and exists and supported until today.
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A data architecture, in part, describes the data structures used by a business and its computer applications software. Data architectures address data in storage, data in use, and data in motion; descriptions of data stores, data groups, and data items; and mappings of those data artifacts to data qualities, applications, locations, etc.