Big Data and Distributed Databases Research Paper

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Big data refers to a type of set of information that requires memory beyond the capacity of the conventional relational databases. The nature of such an assortment of information makes it difficult for ordering to allow easy capture, access, administration, and updates. Traditional databases do not permit one to record, run and process any low-latency high volumes of materials. An example of big data includes the statistics that is utilized on databases of social media sites such as Facebook each day. This large bulk of information arises from the photos, videos, and messages that are shared across the platforms. The set-up also encompasses innovations and techniques that incorporate statistics that are remarkably diverse, such as varieties and technologies that are massive for skills (Misaki et al,. 2017). Since big data covers a wide variety of information types and cannot be defined based on a specific parameter, it is categorized into structured, semi-structured, and unstructured types. Thus, it is generally critical in the modern world and greatly influences almost all aspects of humans’ everyday life.

Modern programs and data applications need high scalability to cope with high requests and outputs; this performance expectation means the need for quicker access of the network-based resources. Today, companies are opting to have their systems and software distributed across several computers in different or same locations. Such databases are referred to as distributed database systems, also known as distributed database management systems (DDBMS). Such systems rely on replication technique, which provides for consistency across all sites by ensuring that any changes made in one location are identified and effected across all others (Misaki, et al. 2017). In comparison to big data, this technology uses duplication concept to ensure that a master database is copied to the rest of the locations so that records exist. Distributed databases can either be homogeneous, being further classified under autonomous and non-autonomous, or heterogeneous, which can be federated or multi-database. The high information transfers that people enjoy today across the world largely rely on distributed databases.

Reference

Misaki, M., Tsuda, T., Inoue, S., & Sato, S. (2017). IEEE Transactions on Semiconductor Manufacturing, 99(1), 1-1. Web.

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IvyPanda. (2022, October 14). Big Data and Distributed Databases. https://ivypanda.com/essays/big-data-and-distributed-databases/

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