A transnational beverage and brewing company with 400+ beer brands, comprising of 2,00,000+ employees in 90+ countries and a huge customer base.


Key Challenges

High data volume: Huge amounts of unstructured data from social networking sites


Data consolidation from various third-party data sources

The Impact


of the effort involved was saved by automated data validation


The Solution

End-to-End automated data testing

Our end-to-end big data validation not only helped to validate all forms of data conversion but also test them early and save huge costs and reduce time-to-market

  • Implemented separate big data platforms (Hadoop cluster) for social data processing
  • Deployed big data technology and test strategy to ensure the successful consolidation and implementation of huge data coming from numerous sources
  • Executed robust functional and user interface testing including look and feel testing
  • Improved test coverage with data validation conducted for various brands, countries, calendar periods, etc.
  • Conducted reuse repository of Structured Query Language (SQL) and test cases
  • Ensured clean and quality data in reports via 100 percent data validation for all reports

Automated E2E Test conversions

100% data coverage with validations conducted for various categories of brands by data conversion techniques

Key highlights of the solution

  • Data ingestion validation
  • Hadoop Distributed File System (HDFS) data load / data validation / map reduces validation
  • Hive validation
  • Data visualization validation
  • Mobile validation
Optimal data testing coverage for a data lake implementation


Leveraging Database Virtualization for Test Data Management

In this paper we explore the concept of database virtualization, industry tool features, and how these tools can be leveraged to improve test data management.

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