A low-code/no-code engineering utility to build and deploy Data pipelines
Simplifying Data Quality & Governance for Reliable Analytics
xWatch provides comprehensive data surveillance capabilities by ensuring data quality and integrity across the entire data lifecycle. It offers data quality assurance by regularly monitoring and improving data quality to support accurate reporting and reliable analytics. Users can develop data quality and profiling rules through manual definitions and AI/ML-driven recommendations. The tool also consolidates data lineage and maps data flow within data lakes to maintain visibility and control over data movement and transformation. Regular audit, balance, and control checks are conducted to maintain data accuracy, while anomaly detection and alerts continuously monitor for irregularities, triggering immediate alerts to address potential issues.
Data Quality Assurance
Completeness
Measures whether data entries are complete, including checks for completeness and the presence of any missing values.
Distinctness
Evaluates the uniqueness of data values, including the count of distinct values and their distribution.
Consistency
Assesses the uniformity of data, ensuring correlations, non-negative values, primary key constraints, and mutual information align with expectations.
Distinctness
Verifies data against expected patterns, ranges, and specific conditions (for example, greater than a value within a range).
Accuracy
Examines statistical measures such as minimum/maximum values, standard deviation, quantiles, sums, means, data type compliance, and length constraints to ensure precision.
Uniqueness
Confirms that data values are unique where required and calculates ratios of unique values.
Anomaly Detection and Alerts
- Monitor data streams in real-time
- Identify patterns and trends
- Trigger alerts or actions as soon as anomalies are detected
Governance
- Establish and monitor quality standards with customizable rules
- Ensure compliance through automated quality checks and audits
- Enable proactive quality improvements with AI/ML-driven insights
- Enhance data integrity through routine profiling and validation
Audit Balance and Control
- Source Count
- Threshold Check
- Header and Trailer Check
- Hash Total Check [ Column Level]
- Financial Sum Totals
Establish Data Lineage
- Map data flow across the entire data lifecycle
- Track source-to-destination transformations and data origins
- Document data dependencies to prevent inconsistencies
- Enable data visibility for audits and compliance