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    Big data testing
    We help you manage and maintain big data competently

BIG DATA Testing

The outburst of enormous digital data characterized by volume, variety and velocity of creation contributes to the evolution of Big data. This voluminous data comprises of both structured and unstructured forms of data and with the rapid proliferation of data, grows bad data.


How critical is testing this big data!
  • The objective of big data testing is to test data and processing integrity and verify data completeness, ensure data transformation and data quality.

  • Data quality involves checking various characteristics like conformity, accuracy, duplication, consistency, validity, etc.

  • 99 Percentage's Big Data testing team aims to provide rapid localization of data issues between points

  • 99 Percentage offers Automated Big Data testing solution that helps you verify structured and unstructured data sets, schemas, approaches and inherent processes residing at different sources in your application.

  • 99 Percentage's highly skilled testing resources can efficiently test the processing of data, be it in batch, real time or interactive, using commodity cluster and other supportive components.

Challenges

  • Heterogeneous and unstructured data spread across different layers

  • Continuous explosion of data and information resulting in bad data

  • Difficult business processes due to complicated business logic

  • Ineffective decision making due to bad or poor data

  • Increased cost of handling variety, volume and velocity of large data sets

  • Performance issues due to heightened data volumes

Benefits

  • Live data integration testing efficiency

  • Big data transperancy to uncover hidden values

  • To help you with more efficient work environments

  • Data collection & Deployment will be tested instantly which will reduce downtime

  • Have your data secured & Ensure scalability

  • Stay ahead with the quality improved on data & performance

Our Offerings

  • Test automation for big data testing

  • Ensure the large data sets across multiple sources are integrated accurately to provide real-time information

  • Certify the quality of frequent data deployments to avoid incorrect decisions and subsequent actions

  • Align the data with changing dynamics to take predictive actions

  • Enable leveraging the right insight from the minutest data sources

  • Ensure scalability and processing of data across different layers and touch-points

Tools Used

Hadoop and MapReduce