Data Quality

Trust Your Data

Quality scoring across five dimensions, schema enforcement, and self-healing SQL across every pipeline layer. Surface data issues alongside pipeline execution.

Quality Dimensions

Four dimensions that define whether your data is ready for analytics.

Completeness

How many values are non-null across your dataset.

Null percentage per column
Required field enforcement
Row count vs. expected thresholds

Uniqueness

Duplicate detection across key columns.

Duplicate row detection
Primary key uniqueness validation
Cross-column composite key checks

Validity

Whether values conform to expected formats and ranges.

Type conformance (dates, numbers, strings)
Range and boundary checks
Regex pattern matching

Freshness

How recently the data was updated relative to expectations.

Last ingestion timestamp
Stale data alerting
Pipeline execution frequency

Quality Scores

Tables are scored from 0 to 100 across completeness, uniqueness, validity, consistency, and freshness — combining into a single number you can track over time.

90 — 100

Excellent

Data is clean, complete, and fresh. Safe for dashboards.

70 — 89

Acceptable

Minor issues. Review flagged columns before using in Gold.

0 — 69

Needs Attention

Significant quality issues. Investigate before proceeding.

Automated Profiling

Profiling runs automatically when data is ingested. Every column gets statistical analysis without manual configuration.

Column-Level Statistics

Min, max, mean, median, standard deviation, null percentage, and distinct count for every column.

Type Distribution

Breakdown of inferred vs. actual types. Catches mixed-type columns (e.g., strings in a numeric field).

Value Frequency

Top values and their frequencies per column. Useful for spotting unexpected categories or outliers.

Sample Preview

View sample rows alongside statistics. Profiles run against your actual data, not a separate sample.

Schema Enforcement & Self-Healing SQL

The platform enforces safe schema evolution and auto-corrects SQL errors at runtime — fixing column references, type mismatches, and conversion errors automatically so pipelines recover without manual intervention.

Integrated Across Every Layer

Quality checks are embedded in the pipeline, not bolted on after.

Bronze Layer

Ingestion

Profile raw data on ingestion. Catch source issues (missing fields, type changes) before they propagate.

Silver Layer

Transformation

Validate transformations produced correct output. Check that deduplication and cleaning worked.

Gold Layer

Analytics

Verify aggregated metrics are within expected bounds. Prevent bad data from reaching dashboards.

Pipeline Execution

Orchestration

Quality checks run as part of the DAG. If a table fails validation, downstream nodes can be paused.

Quality built in

Stop discovering bad data in your dashboards.

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Profiling and scoring run on every ingestion. No separate tool to configure.

OptimaFlo

AI-native data platform. From raw data to business dashboards powered by Apache open standards, visual pipeline building, and AI agents that handle the heavy lifting.