Have you ever loaded invalid data through your pipeline?

DQOps was designed for monitoring distributed environments.
The DQOps Data Observability supports distributed agents that will run in monitored clouds. Observe Data Quality across different public and private clouds

DQOps was designed for monitoring distributed environments.
The DQOps Data Observability supports distributed agents that will run in monitored clouds. Observe Data Quality across different public and private clouds

Developer friendly

Developer friendly

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

DQOps is a developer-friendly data quality 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
  • Edit data quality rules in popular code editors

Source Data Quality checks

Source Data Quality checks

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

DQOps features data quality checks that verify the most common data quality issues. Simply connect to the data source, enable the required quality checks, and verify the 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 determine if it is working properly

Simply migrate your pipelines to a production environment, run pipelines and DQOps data quality checks to ensure 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

DQOps 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

DQOps, 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

DQOps is an open-source platform, 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 according to you needs
  • Data quality rules are easy to version