Outgrown Great Expectations? We Built What Comes Next.
Why Us
As data complexity grows, so do the headaches. Manual rule-writing, batch-only limitations, and a lack of enterprise features create bottlenecks that slow data teams down.
If you’re a data practitioner who’s ready to upgrade to a proactive platform that helps you detect issues and collaborate across business and technical teams, then level up to Qualytics, the enterprise data quality platform built for automation, speed, and scale.
Schedule a Demo
See how Qualytics can transform your data quality workflows.
Hear from Our Customers
Advantage
The Qualytics Advantage: Purpose-Built for Data Practitioners
Qualytics eliminates the guesswork and gruntwork of data quality management with a scalable platform that automates 95% of rule management with ML, centralizes rule management from basic technical to advanced business rules, empowers business and technical teams to collaborate through a simple no-code UI, and drives complex downstream remediations based on data product concepts.
Prevent data issues before they happen
Qualytics uses AI to automatically detect anomalies before they impact your business.
Scale from pilot to enterprise within hours
From SaaS to on-prem, Qualytics meets you where you are and handles large volumes, complex rules, and hybrid architectures.
Make data quality a team sport
Qualytics uses AI to automatically detect anomalies before they impact your business.
Remove the complexity
Spin up complex data quality rules with just a few clicks; no coding or engineering tickets required.
Comparison
Qualytics vs. Great Expectations
As data complexity grows, so do the headaches. Manual rule-writing, batch-only limitations, and a lack of enterprise features create bottlenecks that slow data teams down.
AI Curated Checks
Requires manual management of YAML-based expectations per dataset or column
Proactive Remediation Capabilties
Focuses on validation; remediation is external to the tool and must be custom-built
Breadth of Check Categories
Offers traditional data validation checks (nulls, ranges, patterns)
Data Steward Focused User Experience
CLI- and code-first experience, requiring Python expertise
Audit and Traceability
Manual logging and custom implementation for audit trails
Enterprise Scalability & Performance
Scales through custom pipelines; enterprise support is community-driven or commercial via partners
Deployment Model and Architecture
Open-source and script-driven; infrastructure responsibility lies
AI Curated Checks
Auto-infers checks from historical data patterns and continuously adapts them over time
Proactive Remediation Capabilties
Tools for proactive remediation & workflow tracking for anomalies directly in the platform
Breadth of Check Categories
Covers eight comprehensive data quality dimensions; timeliness, uniqueness, and referential integrity, with AI-driven profiling
Data Steward Focused User Experience
Web-based low/no-code UI with native event-driven actions supporting push and pull integrations (fully open API)
Audit and Traceability
Automatically maintains time-stamped lifecycle logs of quality checks, anomalies, and remediations
Enterprise Scalability & Performance
Designed from the ground up for enterprise workload; auto-scaling, high availability and performance monitoring
Deployment Model and Architecture
Kubernetes-native, supports both hosted (managed VPC) and on-prem (via Helm chart) deployments with enterprise-grade isolation
See Why Enterprises Choose Qualytics Over Great Expectations
