Professional Quality Assurance Services for Superior Product Delivery

We are aware of the dangers that your analytics reports may encounter. With expertise in both proprietary and open-source tools. Among our data testing services are

Production Data Validation: Your data warehouse must provide your company with the greatest competitive advantage possible. Our ETL Testing and Validation methods guarantee production reconciliation.

Application Upgrade Testing: Technology evolves on a daily basis. And, in order to be compliant, your data warehouse must adapt to the changes and adopt new security and performance enhancements. With our systematic testing method to move existing data into the new repository and the like, we can save significant work in pre- and post-upgrade.

Validating the Source-to-Target Relationship: Data correctness is an important check that must be conducted both during and after the data transformation process. Our automated testing approach not only checks the end-to-end data but also recommends corrective actions to avoid future data corruption.

Testing for Data Completeness: After validating the data with Validation testing, we ensure that all of the data is accurately loaded into the data warehouse by comparing validation counts, aggregates, and Spot checks between random Actual and Target data on a regular basis.

 Metadata Testing: Metadata safeguards data quality. Our automated metadata testing technique comprises a thorough examination of Data type, Data length, Index /Constraint, and so on.

Data Transformation Testing: Data transformation testing can be difficult at times since several SQL queries may need to be executed to ensure that all transformation rules correspond to business rules. Our methods ensure that you save time on this time-consuming activity.

Our broad range of services include

Pre-ETL Validations: Format, Consistency, Completeness
Post-ETL Tests: Meta-data, Data transformation, Data quality checks, Business validations
Validate Models: Implementation, Computation
Validate Aggregation: Data Hierarchy, Data Scope, Summarized Values
Validate Visualization: Information Representation, Data Format, Result Intuitiveness