Track migration progress
Once the data quality in the target platform matches your quality standards, the data can be deemed migrated. As the quality is assessed for each entity, these numbers provide valuable insights into the progress of the migration project.
Continuous reporting
Automated updates keep your data quality reports current. Triggered by CI/CD pipelines or as needed, these reports provide accurate and timely information, eliminating the risk of relying on outdated data.
Multi level quality monitoring
Data quality checks can be customized to cover various levels of scrutiny, from rapid high-level assessments such as schema and row count checks to detailed cell-level equality validations.
Designed with data engineers in mind
Data engineers can delve deeper into potential issues and obtain comprehensive information related to failed validations. Clues are provided to assist in pinpointing the areas that require further investigation. To facilitate this process, a dataset will be made available, featuring examples of data mismatch.
Scalable to large datasets
For large datasets, extensive validations can be scaled out either through serverless solutions like Cloud Functions and Cloud Run or by employing autoscaling Kubernetes solutions
Variable quality standard
Data quality contracts can be specified for each entity at different levels. Prioritize your most valuable data.
