QualiFlow – Intelligent Data Quality Management

QualiFlow – Intelligent Data Quality Management is Rysun Labs’ next-generation platform for ensuring trusted, automated data validation across cloud and enterprise ecosystems. Built on an open, modular, and cloud-native architecture, QualiFlow DQM separates the Control Plane (UI) from the Validation Engine, enabling scalable, high-performance data quality enforcement without disrupting data pipelines or user workflows.

Designed for modern data platforms, QualiFlow DQM helps data engineers, analytics teams, and governance stakeholders define, execute, and monitor data quality rules consistently across distributed data environments.

Key Capabilities

• Rule-Based Data Validation Define and execute reusable quality checks for null values, pattern validation, schema integrity, record counts, and referential consistency across datasets.

• Great Expectations Integration Native integration with Great Expectations enables configurable, industry-standard validation rules and reusable expectation libraries, ensuring transparency and avoiding vendor lock-in.

• Multi-Source Connectivity Validate data across Snowflake, Amazon Redshift, PostgreSQL, BigQuery, and Amazon S3, supporting both cloud data warehouses and data lake architectures.

• Data Quality Observability Monitor rule execution status, pass/fail rates, and data reliability trends through real-time dashboards and historical run analysis.

• AWS-Native Deployment Deployable on Amazon EC2, ECS, or EKS, with Amazon RDS for metadata storage, Amazon S3 for rule artifacts and logs, and Amazon CloudWatch for monitoring and operational visibility.

Business Benefits

• Up to 70% reduction in manual data validation effort
• 90–95% data accuracy achieved in initial validation cycles
• Improved trust in analytics, reporting, and AI/ML inputs
• Faster detection and resolution of data quality issues across pipelines
• Better alignment with data governance and compliance requirements

Key Use Cases

• ETL and post-ingestion data quality validation
• Data pipeline certification before downstream consumption
• Data mesh domain-level quality governance
• Compliance monitoring and audit readiness
• Ensuring reliable input data for AI and machine learning workloads

Future Roadmap

• Generative AI–based conversational rule assistant
• Integration with AWS Glue Data Catalog for metadata-driven validation
• Federated Quality Mesh for distributed, domain-owned quality enforcement