Data Observability For Data Engineers

Prevent loading invalid data through your data pipelines

Have you ever loaded invalid data through your pipeline?

Data Observability solves the problems with invalid data by continuously monitoring the quality of data and detecting outliers

Developer friendly

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

Source Data Quality checks

Source Data Quality checks

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

DQO.ai comes with Data Quality checks that will validate the most popular Data Quality issues. Simply connect to your data source, enable required quality checks and verify your source data.

  • CI\CD friendly
  • Built-in standard Data
  • Quality checks
    Integration with popular data warehouses

Pipeline Data Quality checks

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

Data lineage protection

Data lineage protection

Provide necessary visibility and context into an organization's data


DQO.ai enables you to monitor Data Quality on each step of: data source, loading data into your pipeline, retrieving data from your pipeline, etc.

  • Target the source of errors
  • Make sure each step of data ingestion works properly
  • Double check your data before using it

Hold data loading

Hold data loading

Increase awareness and control over your data

DQO.ai, based on the rules, assigns significance for the invalid data. If an alert is important, you can undertake suitable actions, e. g. hold the pipeline and verify the problem, before the data is loaded in your database.

  • Increase Data Quality in your database
  • Work more efficiently with your schedulers
  • Improve your data pipeline

Customizable Data Quality checks

Customizable Data Quality checks

Define and develop your own SQL checks with Python rules


DQO.ai is an open-source tool, so it enables you to come up with your own ideas for quality checks. Data Quality rules that are defined in text files are easy to store in the code repository. No deployment is required to update the Data Quality checks.

  • Store Data Quality rules in Git
  • Rebuild existing rules for you needs
  • Data Quality rules are easy to version

No one can understand your data like we do!