BibTeX:

@article{IJIRSTV4I10010,
     title={Big Data Quality Assurance and Testing Framework},
     author={Anita Kamdi},
     journal={International Journal for Innovative Research in Science & Technology},
     volume={4},
     number={10},
     pages={62--67},
     year={},
     url={http://www.ijirst.org/articles/IJIRSTV4I10010.pdf},
     publisher={IJIRST (International Journal for Innovative Research in Science & Technology)},
}

As more and more Big Data applications are be-coming the industry adopted standard and in order to enable economy of scale, are being fully automated, less and less human involvement is required. It becomes increasingly important to ensure that automated Big Data processes are operating correctly and ensure that organizations and individuals whose lives are impacted by its algorithms are treated fairly. This paper attempts to establish how the designers, architects, system analysts, testers, business analysts, IT auditor can benefit from a generalized approach towards establishing a quality assurance framework for big data quality assurance testing. Testing big data is one of major challenges industry is facing now a days as organizations struggle to decide upon amount of testing required on target data. This results in undesired data being processed to production leading to more cost and time. To overcome this more defined approach is required for validation and verification of data early in the lifecycle.

Big Data, Quality Assurance, Bloom Filter Per-formance Optimization, Map Reduce, Test Guidelines