Qualytics

Outgrown Great Expectations? We Built What Comes Next.

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.

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See how Qualytics can transform your data quality workflows.

Hear from Our Customers

"Qualytics user experience"

As a user of Qualytics, I am impressed by its robustness and high configurability. The tool excels in allowing the export of anomalies and provides a data preview feature that helps visualize results effectively. One of the standout features is the ability to export anomalies directly to the customer’s data source, which is incredibly useful. Additionally, the support provided is exceptional, with a high rate of attendance and responsiveness. The team keeps enhancing the tool and its integrations.

– Sabrina G.

Data governance specialist

Enterprise (> 1000 emp.)

"Intuitive and powerful"

Intuitive interface, powerful ML to create immediate data quality checks. Easy to create new rules, monitor data health. Extremely responsive dev team. New features arrive at lightening speed.
Implementation takes 20 minutes or less.

– Renee C.

Global Data Quality Lead at Revantage

Mid-Market (51-1000 emp.)

"A Refreshing Partnership: Achieving Data Quality with Qualytics"

Working with Qualytics has been a breath of fresh air. They are an amazing team to work with and extremely focused on helping companies implement Data Quality. Their approach is process oriented, which is where you succeed with Data Quality, and their tooling supports secure, cost effective approach that any size of company can get behind. Implementation and upgrades are well tested and easy to execute. The team is dedicated to amazing customer support. From the beginning they were there to listen and to walk with us through the process. In short, I wish all software vendors were like Qualytics.

– Verified User in Oil & Energy i

Enterprise (> 1000 emp.)

"Great tool for Data Quality Management"

  • Very easy for business users to create their own quality rules with just a few clicks.
  • Integration with databricks is seamless.
  • Allows for all the common data quality tasks such as profiling operations, scanning operations, quality checks, and quality check suggestions.
  • More advanced quality checks such cross-table validations are also supported.
  • Good API integration support which makes possible the integration with orchestrators like Airflow.
For us it checked all the boxes of what we were expecting of a data quality management tool.

– Francisco M.

Director of Data Engineering

Mid-Market (51-1000 emр.)

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 ML 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

Give technical teams and business users a shared space to manage, monitor, and act on data quality.

Remove the complexity

Spin up complex data quality rules with just a few clicks; no coding or engineering tickets required.

Schedule a Demo

Qualytics vs. Great Expectations

With Qualytics, you get a unified data quality platform that adapts to your environment, empowers your team, and scales with your enterprise.

Features

AI Curated Checks
Proactive Remediation Capabilties

Breadth of Check Categories
Data Steward Focused User Experience
Audit and Traceability
Enterprise Scalability & Performance
Deployment Model and Architecture
Requires manual management of YAML-based expectations per dataset or column
Focuses on validation; remediation is external to the tool and must be custom-built
Offers traditional data validation checks (nulls, ranges, patterns)
CLI- and code-first experience, requiring Python expertise
Manual logging and custom implementation for audit trails
Scales through custom pipelines; enterprise support is community-driven or commercial via partners
Open-source and script-driven; infrastructure responsibility lies with the user

Auto-infers checks from historical data patterns and continuously adapts them over time

Tools for proactive remediation & workflow tracking for anomalies directly in the platform
Covers eight comprehensive data quality dimensions; timeliness, uniqueness, and referential integrity, with AI-driven profiling
Web-based low/no-code UI with native event-driven actions supporting push and pull integrations (fully open API)
Automatically maintains time-stamped lifecycle logs of quality checks, anomalies, and remediations
Designed from the ground up for enterprise workload; auto-scaling, high availability and performance monitoring
Kubernetes-native, supports both hosted (managed VPC) and on-prem (via Helm chart) deployments with enterprise-grade isolation

Features

AI Curated Checks
Proactive Remediation Capabilties

Breadth of Check Categories
Data Steward Focused User Experience
Audit and Traceability
Enterprise Scalability & Performance
Deployment Model and Architecture

Auto-infers checks from historical data patterns and continuously adapts them over time

Tools for proactive remediation & workflow tracking for anomalies directly in the platform
Covers eight comprehensive data quality dimensions; timeliness, uniqueness, and referential integrity, with AI-driven profiling
Web-based low/no-code UI with native event-driven actions supporting push and pull integrations (fully open API)
Automatically maintains time-stamped lifecycle logs of quality checks, anomalies, and remediations
Designed from the ground up for enterprise workload; auto-scaling, high availability and performance monitoring
Kubernetes-native, supports both hosted (managed VPC) and on-prem (via Helm chart) deployments with enterprise-grade isolation
Requires manual management of YAML-based expectations per dataset or column
Focuses on validation; remediation is external to the tool and must be custom-built
Offers traditional data validation checks (nulls, ranges, patterns)
CLI- and code-first experience, requiring Python expertise
Manual logging and custom implementation for audit trails
Scales through custom pipelines; enterprise support is community-driven or commercial via partners
Open-source and script-driven; infrastructure responsibility lies with the user

Ready to level up your data quality?

Schedule a Demo
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