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Declarative Configuration
Allows users to define monitoring rules using simple configuration files for easy setup.
Dynamic Tagging
Automatically categorizes and tags data assets based on attributes for better organization.
Reusable Custom Monitoring
Enables teams to create and apply custom monitoring rules across multiple datasets.
Dimension Level Insights
Provides granular data quality insights at the dimension level for detailed analysis.
Multi column Checks
Validates relationships between multiple columns to detect inconsistencies and errors.
Data Quality Monitoring Details
Offers in-depth tracking of data health, freshness, completeness, and accuracy.
Row Level Debugging
Pinpoints issues at the row level, making it easier to investigate and resolve errors.
Root Cause and Impact Analysis
Identifies the source of data issues and assesses their impact on business decisions.
Anomaly Detection Engine
Uses machine learning to detect unusual patterns and prevent data failures.
Flexible Control
Provides customizable monitoring and alert settings to fit specific business needs.
Brand Name | Bigeye |
Information | Bigeye |
Founded Year | 2019 |
Director/Founders | Egor Gryaznov, Kyle Kirwan |
Company Size | 1-100 Employees |
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