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
