Automating ETL testing and data validation has been a gap in the industry. BEAT™ has a unique way to tackling this problem by building a solution that tackles this from the data consumers perspective. BEAT™ also moves data integration work to a BDD methodology for ETL stories.
Ensuring sanctity of data post deployment is critical for business adoption of your Big Data platform. Trust in data needs to be cultivated. Data validation is more than testing during development phase. The approach recommended is to generate the data validation scores post production runs. BEAT provides the hooks and checks to ensure quality consistency of both data and business KPI.
BEAT builds the common checks for data sanity that often, if not caught early enough cause a ripple effect leading to the wrong data showing up in the reports. Audits are captured and stored in a repository by runs, so that you can look in the mirror and see if ever there was a data issues left unchecked in the near past.
Typically test automation is done for regression testing, what BEAT provides is test automation for engineering during the sprint. Leveraging our experience in the data space for more than 3000 man years, we have built the solution which helps you with your time to market for implementing Big Data solutions with trusted data.