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MongoDB provides three ways to perform aggregation: the aggregation pipeline, the map-reduce function and single-purpose aggregation methods. [ 40 ] Map-reduce can be used for batch processing of data and aggregation operations.
MapReduce is a programming model and an associated implementation for processing and generating big data sets with a parallel and distributed algorithm on a cluster. [1] [2] [3]A MapReduce program is composed of a map procedure, which performs filtering and sorting (such as sorting students by first name into queues, one queue for each name), and a reduce method, which performs a summary ...
NoSQL (originally referring to "non-SQL" or "non-relational") [1] is an approach to database design that focuses on providing a mechanism for storage and retrieval of data that is modeled in means other than the tabular relations used in relational databases. Instead of the typical tabular structure of a relational database, NoSQL databases ...
C#, Java, Python, Node.js, JavaScript, SQL: Platform-as-a-Service offering, part of the Microsoft Azure platform. Builds upon and extends the earlier Azure DocumentDB. Yes DocumentDB: Amazon Web Services: Proprietary online service various, REST: fully managed MongoDB v3.6-compatible database service Yes DynamoDB: Amazon Web Services: Proprietary
According to computer scientist Eric Brewer of the University of California, Berkeley, the theorem first appeared in autumn 1998. [9] It was published as the CAP principle in 1999 [10] and presented as a conjecture by Brewer at the 2000 Symposium on Principles of Distributed Computing (PODC). [11]
Map/Reduce Views and Indexes The stored data is structured using views. In CouchDB, each view is constructed by a JavaScript function that acts as the Map half of a map/reduce operation. The function takes a document and transforms it into a single value that it returns.
Database normalization is the process of structuring a relational database in accordance with a series of so-called normal forms in order to reduce data redundancy and improve data integrity. It was first proposed by British computer scientist Edgar F. Codd as part of his relational model.
It is designed to provide high availability, scalability, and low-latency access to data for modern applications. Unlike traditional relational databases, Cosmos DB is a NoSQL (meaning "Not only SQL", rather than "zero SQL") and vector database, [1] which means it can handle unstructured, semi-structured, structured, and vector data types. [2]