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  2. Database scalability - Wikipedia

    en.wikipedia.org/wiki/Database_scalability

    Database scalability is the ability of a database to handle changing demands by adding/removing resources. Databases use a host of techniques to cope. [ 1 ] According to Marc Brooker: "a system is scalable in the range where marginal cost of additional workload is nearly constant."

  3. Graph database - Wikipedia

    en.wikipedia.org/wiki/Graph_database

    Aerospike Graph is a highly scalable, low-latency property graph database built on Aerospike’s proven real-time data platform. Aerospike Graph combines the enterprise capabilities of the Aerospike Database - the most scalable real-time NoSQL database - with the property graph data model via the Apache Tinkerpop graph compute engine.

  4. NewSQL - Wikipedia

    en.wikipedia.org/wiki/NewSQL

    NewSQL is a class of relational database management systems that seek to provide the scalability of NoSQL systems for online transaction processing (OLTP) workloads while maintaining the ACID guarantees of a traditional database system.

  5. NoSQL - Wikipedia

    en.wikipedia.org/wiki/NoSQL

    Instead of the typical tabular structure of a relational database, NoSQL databases house data within one data structure. Since this non-relational database design does not require a schema, it offers rapid scalability to manage large and typically unstructured data sets. [2]

  6. Scalability - Wikipedia

    en.wikipedia.org/wiki/Scalability

    Scalability is the property of a system to handle a growing amount of work. One definition for software systems specifies that this may be done by adding resources to the system. [1] In an economic context, a scalable business model implies that a company can increase sales given increased resources. For example, a package delivery system is ...

  7. CAP theorem - Wikipedia

    en.wikipedia.org/wiki/CAP_theorem

    Note that consistency as defined in the CAP theorem is quite different from the consistency guaranteed in ACID database transactions. [4] Availability Every request received by a non-failing node in the system must result in a response. This is the definition of availability in CAP theorem as defined by Gilbert and Lynch. [1]

  8. Data-intensive computing - Wikipedia

    en.wikipedia.org/wiki/Data-intensive_computing

    Data-parallelism applied computation independently to each data item of a set of data, which allows the degree of parallelism to be scaled with the volume of data. The most important reason for developing data-parallel applications is the potential for scalable performance, and may result in several orders of magnitude performance improvement.

  9. PostgreSQL - Wikipedia

    en.wikipedia.org/wiki/PostgreSQL

    PostgreSQL can link to other systems to retrieve data via foreign data wrappers (FDWs). [46] These can take the form of any data source, such as a file system, another relational database management system (RDBMS), or a web service. This means that regular database queries can use these data sources like regular tables, and even join multiple ...