Do you monitor data quality across all databases according to the same process?
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
Unified Data Quality
Unified Data Quality
Monitor the quality of all your databases in one place
Connect all data sources to DQOps and monitor the same quality measures. Monitor popular data quality dimensions such as validity, availability, reliability, timeliness, uniqueness, reasonableness, completeness and accuracy.
- Analyze data quality across the enterprise
- Detect data quality issues at multiple dimensions
- Compare data quality metrics across different databases
Agreed Data Quality Rules
Agreed Data Quality Rules
Verify the Data Quality of all databases and Data Lakes with the same set of Data Quality rules.
Select a subset of data quality checks that should be enabled for all databases. Define additional custom data quality checks or modify built-in data quality checks to meet your requirements and policies.
- Measure all your databases with the same rules, regardless of the type of database or Data Lake you use
- Customize data quality rules to meet your unique needs
- Define custom quality checks as templated SQL queries (Jinja2 compatible), Python code or Java classes for the most advanced scenarios
Data Quality Documentation
Data Quality Documentation
Document data quality rules for your databases as easy-to-understand, shareable YAML files
Define data quality checks as DQOps data quality specifications. The specifications are easy to read and clearly show the type of checks and their expected thresholds for different alert severity levels.
- Use data quality specification files as data quality documentation
- Store data quality specification files in a code repository along with your data science and data pipeline code
- Track changes in data quality requirements by comparing specification files in Git
Compare the same metrics
Compare the same metrics
Ensure that all database teams are monitoring the same dimensions of data quality
Introduce the same data quality checks across the whole organization. Let every data team use and measure the same data quality checks, tracking the same data quality KPIs.
- Use the same data quality dimensions across the organization
- Compare data quality KPIs in the same way across databases
- Improve the overall data quality across the whole enterprise
Simple Data Quality
Simple Data Quality
Convince data teams to apply data monitoring in their data pipelines
DQOps is a second generation data quality monitoring platform that was designed after enabling thousands of data quality checks. DQOps has been redesigned to meet the requirements of both data engineering and data science teams. Data quality monitoring should be so simple that the benefits overcome any initial learning challenges.
- Data quality controls that are easy to understand for data quality novices
- Edit data quality controls with full autosuggestion support in popular code editors
- Ability to make large changes to data quality rules, such as renaming a table or column
Database cross checks
Database cross checks
Analyze Data Quality across different databases by comparing aggregated data
Define accuracy checks (comparison with real data) and semi-accuracy checks (comparison with another dataset that should be equal). Data accuracy checks can take data from disparate sources. DQOps can compare aggregate data for selected dimensions.
- Detect mismatches on different data granularity
- Detect missing partitions and groups of data in different databases, you may not have data for one state in another copy of the database
- Continuously monitor differences
