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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."
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 in a database remains present in all shards, [a] but some appears only in a single shard. Each shard acts as the single source for this subset of data.
Vertical scaling, also known as scaling up, is the process of replacing a component with a device that is generally more powerful or improved. For example, replacing a processor with a faster one. Horizontal scaling, also known as scaling out is setting up another server for example to run in parallel with the original so they share the workload.
Interoperability between disparate clinical information systems requires common data standards or mapping of every transaction. However common data standards alone will not provide interoperability, and the other requirements are identified in "How Standards will Support Interoperability" from the Faculty of Clinical Informatics [2] and "Interoperability is more than technology: The role of ...
Webscale is a computer architectural approach that brings the capabilities of large-scale cloud computing companies into enterprise data centers. [ 4 ] In distributed systems , there are several definitions according to the authors, some considering the concepts of scalability a sub-part of elasticity , others as being distinct.
Within healthcare systems, horizontal integration is generally much more prominent. However, in the United States, major vertical mergers have included CVS Health's purchase of Aetna, and Cigna's purchase of Express Scripts. The integration of CVS Health and Aetna resulted in the combination of one of the nation's largest health insurance ...
The standards allow for easier 'interoperability' of healthcare data as it is shared and processed uniformly and consistently by the different systems. This allows clinical and non-clinical data to be shared more easily, theoretically improving patient care and health system performance. [1]
For example, services like Google, Twitter, Facebook, Amazon, and Netflix exemplify large-scale distributed systems. Here are key considerations: Functional and non-functional requirements; Capacity estimation; Usage of relational and/or NoSQL databases; Vertical scaling, horizontal scaling, sharding; Load balancing; Primary-secondary ...
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