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A Review: Hadoop Map Reduce In Big Data Analytics
| Content Provider | Semantic Scholar |
|---|---|
| Author | Prasanna, B. Alamelu, G. Krishnaveni, K. |
| Copyright Year | 2018 |
| Abstract | The term ‘Big Data’ describes new techniques and technogies to capture, store, distribute, manage and analyze larger-sized datasets with high-speed and different structures. Big data can be structured, unstructured or semi-structured, resulting in failure of expected data management methods. Data is created from various different sources and can appear in the system at various charges. In order to process these large amounts of data in a low-cost and efficient way, parallelism is used. Big Data is a data whose scale. Diversity and density need new design, techniques, algorithms, and analytics to manage it and extract value and hidden knowledge from it. Hadoop is the foundation platform for structuring Big Data, and solves the problem of creation it useful for analytics purposes. Hadoop is an open source software project that enables the distributed processing of large data sets across clusters of product serves. It is designed to level up from a single server to thousands of machines, with a very high degree of fault tolerance. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://www.ijsrcsams.com/images/stories/Past_Issue_Docs/ijsrcsamsv7i5p37.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
