Extensible Data Observability

Increase confidence in your data by tracking the data quality

DQO.ai is a DataOps friendly data observability tool with customizable data quality checks and data quality dashboards

Trust the quality of your data

DQO.ai increases confidence in the quality of the data. Connect data sources, activate trusted data quality checks and and monitor data on DAMA data quality dimensions.

Developer friendly

Detect Data Quality issues in source data before you attempt to load it

DQO.ai 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

Detect Data Quality issues in source data before you attempt to load it

DQO.ai 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.ai 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

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.ai 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.ai

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

New data quality checks may be easily added or built-in checks could be customized to meet the needs

Multi-cloud

Track data integrity across different clouds and on-premise environments by DQO.ai agent-based architecture

How to start working with a Dqo.ai

We would like to introduce you a tutorial how to start using a DQO.ai tool.
Check it out and learn more about how to run examples,  add connections, import, edit tables, and define and run checks.

Why is tracking Data Quality KPIs Important to Your Company?

Development and ultimate success in monitoring data quality for your business are not possible without tracking the progression of results.
In the business world, it is crucial to track even the smallest record, the smallest thing, because everything matters.

No one can understand your data like we do!