DataOps Data Quality
Define data rules using an easy to use web interface and share the results with your business users.
Key Features & Benefits
Automates the testing of data at Rest or in Motion thus by empowering business users, Data Stewards and Data Owners.
Data Quality Score
Business Data Rules
Semantic Data Types
Common Data Model
Reference Data Checks
Data Quality Dashboard
Displays a trend of Data Quality Scores for the enterprise. Users can drill down to review the scores at Data Model, Table, Data Element and Data Quality Dimension levels. Data Quality Score is automatically computed based on the Data Quality Rules for data at rest as data in motion (ETL).
Data Observability for Data Pipeline
By automating the testing of data being ingested in your Data Lake and Data Integration projects, DataOps Data Quality enables Continuous Integration.
- Data Profiling: Profiles the datasets being ingested and maintains a repository of historical values.
- Anomaly Detection: Uses Time Series based algorithms to predict expected value and identifies anomalies
- Grouped metrics: Identifies deviations to business metrics and notifies users.
Connects to all Popular Data Sources
We support your data source in whichever form it is. You think of any kind of data source – whether it is a relational, NoSQL, cloud, or file data source – we support most of them.
Data Quality Dimensions
Support data quality checks for the following DQ dimensions
See DataOps Data Quality in action
Add value to your Data Analytics projects and save money.