ETL Testing & Data Quality Assurance

ETL Testing & Data Quality Assurance

Automated ETL testing, data pipeline validation, source-to-target reconciliation, and data quality assurance at enterprise scale.

The ETL Testing & DQA Advantage

Bad data costs organizations an average of $12.9M annually. DataTrust prevents it.

Move from manual ETL testing to automated data pipeline validation and quality assurance with continuous monitoring and reconciliation.

Why Traditional ETL Testing Falls Short

Manual ETL testing and basic validation can't keep up with modern data pipeline complexity

✕ Manual ETL testing that can't scale with pipeline changes
✕ Silent transformation failures discovered too late
✕ No visibility into data pipeline health
✕ Regression issues after every deployment
✕ Incomplete coverage across ETL jobs
✕ No automated anomaly detection in pipelines

Source-to-Target Reconciliation at a Glance

DataTrust validates data accuracy by comparing source records against target systems. Our intelligent matching handles row-level, aggregate, and balance validations with automatic variance detection.

  • Row-by-row comparison with transformation support
  • Aggregate totals and balance checks
  • Automatic variance detection and reporting
  • Cross-platform reconciliation support

SOURCE

sales_raw
1,250,000 rows
$45.2M total

TARGET

sales_warehouse
1,250,000 rows
$45.2M total

RECONCILED ✓
100% Match Rate
0 Variances

Continuous Data Quality & Reconciliation

See how DataTrust ensures data quality and reconciles data from source to target with automated validation and certification

  1. Connect
    Data Sources
    Connect to databases, warehouses, lakes, and BI tools
    Snowflake, Databricks, BigQuery, Redshift

  2. Profile
    Auto-Discovery
    Automatically profile schemas, patterns, and statistics
    Schema, Patterns, Stats, Samples

  3. Reconcile
    Compare & Balance
    Compare source-to-target data with row and aggregate matching
    Row Match, Aggregates, Balance, Variance

  4. Validate
    Quality Rules
    Apply pre-built, Gen AI powered, and custom rules
    Complete, Accurate, Consistent, Timely

  5. Monitor
    Observability
    Detect anomalies, drift, and freshness issues in real-time
    Anomalies, Drift, Freshness, Volume

Comprehensive ETL pipeline testing, transformation validation, and regression testing

Source-to-Target Validation

Automated validation of data from source systems through transformations to target with row-level, aggregate, and schema checks.

Transformation Testing

Verify ETL transformation logic, business rules, aggregations, and data mappings are executing correctly.

Regression Testing

Automated regression tests for pipeline changes with CI/CD integration to catch issues before deployment.

Pipeline Monitoring

Real-time monitoring of ETL job health, data volumes, latency, and quality metrics across all pipelines.

Comprehensive data profiling, quality rules, and automated validation at scale

Automated Data Profiling

Automatically discover schemas, patterns, statistics, and anomalies across all your data sources.

Data Quality Rules

Pre-built and custom rules for completeness, accuracy, consistency, timeliness, and validity checks.

Anomaly Detection

ML-powered detection of data drift, volume changes, freshness issues, and unexpected patterns.

Quality Dashboards

Executive and operational dashboards showing data health, trends, and SLA compliance metrics.

Source-to-target validation, balance controls, and variance analysis

Data Reconciliation

Compare and validate data across source and target systems with row-level, aggregate, and schema reconciliation.

Balance & Control Totals

Automated reconciliation of financial data with balance checks, control totals, and variance analysis.

Data Certification

Certify data assets as trusted with approval workflows, validity periods, and certification badges.

Pipeline Integration

Native integration with ETL/ELT tools, orchestrators, and CI/CD pipelines for automated quality gates.

AI-powered rule generation, smart remediation, and continuous improvement

Gen AI Rules

AI-powered rule generation that analyzes your data patterns and automatically suggests quality rules tailored to your datasets.

Smart Anomaly Detection

Machine learning models that auto-learn baselines and detect deviations without manual threshold configuration.

Automated Remediation Suggestions

AI suggests data fixes and remediation actions based on historical patterns and business rules.

Data Contracts

Define and enforce data contracts between producers and consumers with automated validation and breaking change alerts.

End-to-End Data Quality & Reconciliation

A comprehensive platform for data quality validation, source-to-target reconciliation, and certification across your entire data estate

Core Capabilities

  • Automated Data Profiling
  • Data Reconciliation
  • Data Quality Rules
  • Gen AI Rules
  • Balance & Control Totals
  • Anomaly Detection
  • Real-Time Monitoring
  • Data Certification
  • Pipeline Integration
  • Issue Management
  • Quality Dashboards
  • Data Contracts

Where DataTrust Excels

DQA Automation
QC Testing at Scale
Source-to-Target Reconciliation
Financial Data Balancing
Data Migration Validation
Regulatory Compliance
Data Certification
AI/ML Data Validation

Measurable Impact

  • 90% Fewer data incidents
  • 70% Faster issue resolution
  • 5x More data coverage
  • 50% Less manual testing
  • 99.9% Issue detection rate
  • 100% Coverage at scale

Ready to Automate ETL Testing & Data Quality?

See how DataTrust can help you automate ETL testing, data pipeline validation, reconciliation, and data quality assurance at scale.