A low-code/no-code engineering utility to build and deploy Data pipelines
Data Quality Assurance
Completeness
Measures whether data entries are complete, including checks for completeness and the presence of any missing values.
Consistency
Assesses the uniformity of data, ensuring correlations, non-negative values, primary key constraints, and mutual information align with expectations.
Accuracy
Examines statistical measures such as min/max values, standard deviation, quantiles, sums, means, data type compliance, and length constraints to ensure precision.
Distinctness
Evaluates the uniqueness of data values, including the count of distinct values and their distribution.
Distinctness
Verifies data against expected patterns, ranges, and specific conditions (e.g., greater than a value, within a range).
Uniqueness
Confirms that data values are unique where required and calculates ratios of unique values.
Anomaly Detection and Alerts
Continuously monitor data for irregularities, triggering alerts to address potential issues promptly.
- Monitor data streams in real-time
- Identify patterns and trends
- Trigger alerts or actions as soon as anomalies are detected
Governance
Audit Balance and control
Regularly perform audits and balance checks to maintain data accuracy and integrity.
- Source Count
- Threshold Check
- Header and Trailer Check
- Hash total Check [ Column Level]
- Financial Sum Totals
Establish Data Lineage
Map the data flow within Data Lakes to maintain visibility and control over data movement and transformation.
