Extensible Data Quality monitoring
Increase confidence in your data by monitoring data quality
DQO is a DataOps friendly data quality monitoring tool with customizable data quality checks and data quality dashboards
Extensible Data Quality monitoring
Increase confidence in your data by monitoring data quality
DQO is a DataOps friendly data quality monitoring tool with customizable data quality checks and data quality dashboards
Trust the quality of your data
DQO increases confidence in the quality of the data.
Connect data sources, activate data quality checks and monitor data on DAMA data quality dimensions.
Developer friendly
Detect Data Quality issues in source data before you attempt to load it
DQO is a developer friendly data qulity monitoring platform, designed by data engineers for data engineers.
All data quality rules are stored in text files, which you can store in Git along with your scripts. Data quality rules are editable with all popular editors (such as VSCode) using autocomplete.
- Store data quality rules in Git
- Edit data quality rules with a text editor
- Get auto suggestions (autocomplete) of data quality rules
Detect Data Quality issues in source data before you attempt to load it
DQO is a developer friendly Data Observability tool, designed by Data Science engineers for Data Science engineers. All data quality rules are stored in text files that you can store in Git along with your scripts. The Data Quality rules are editable with all popular editors (like VSCode) using autocomplete.
- Store data quality rules in Git
- Edit Data Quality rules with a text editor
- Get auto suggestions (autocomplete) of Data Quality rules
Pipeline Data Quality checks
Detect Data Quality issues in your data pipeline and find out whether it is working properly
Simply migrate your pipelines to the production environment, run the pipelines and DQO data quality checks to ensure a successful data processing.
- Built-in standard data quality checks
- Instantly update data quality rules after migrating pipelines to production environment
- Define data quality tests to be executed after migration
Detect Data Quality issues in your data pipeline and find out whether it is working properly
Simply migrate your pipelines to the production environment, run the pipelines and DQO Data Quality checks to ensure a successful data processing.
- Built-in standard data quality checks
- Instantly upgrade the data quality rules after migrating your pipelines to the production environment
- Define Data Quality tests to be executed after migration
Why DQO
DevOps friendly
Store data quality definitions in your repository
Developer Friendly
Configure data integrity checks in any editor as YAML files with code completion
Extensible
Easily customize built-in checks or add new data quality checks.
Multi-cloud
Track data integrity across different cloud and on-premise environments with DQO agent-based architecture
Why DQO
DevOps friendly
Store data quality definitions in your repository
Developer Friendly
Configure data integrity checks in any editor as YAML files with code completion
Extensible
Easily customize built-in checks or add new data quality checks.
Multi-cloud
Track data integrity across different cloud and on-premise environments with DQO agent-based architecture
How to start working with a Dqo
Check our tutorial on how to start using a DQO platform.
Learn how to add connections, import tables, and define and run checks.
Why is tracking Data Quality KPIs Important to Your Company?
You can’t improve what you don’t measure. Read our Blog on why tracking data quality KPIs is important to your business.
