Towards an Automated Testing Framework for Big Data
Ramanathan Meyappan1, Nachiyappan S2, Disha Nair3
1Ramanathan Meyappan, TCS, Delaware, USA.
2Nachiyappan S, SCSE VIT Chennai (Tamil Nadu), India.
3Disha Nair, Chase Bank, Wilmington USA.
Manuscript received on 18 December 2019 | Revised Manuscript received on 24 December 2019 | Manuscript Published on 31 December 2019 | PP: 479-485 | Volume-9 Issue-1S3 December 2019 | Retrieval Number: A10871291S319/19©BEIESP | DOI: 10.35940/ijeat.A1087.1291S319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (

Abstract: Big data testing services are to deliver end to end testing methodologies which address our big data challenges. The testing module includes two types of functionalities. One is functional testing and second is non- functional testing. The functional testing should be accomplished at every stage of big data processing. Functional testing is nothing but the big data sources extraction testing, data migration testing and big data ecosystem. Testing which completes ETL test strategy, Map job reduce validation, multicore Data integration validation and data duplication check. On the other side the non-functional testing is to ensure that there are no quality defeat in data and no performance related issues. It covers the area for security testing, performance testing which solve the problem of monitoring and identify bottlenecks.
Keywords: Big Data, Functional Testing, Non-Functional Testing, Data Duplication Check.
Scope of the Article: Big Data Analytics and Business Intelligence