Qualytics, the augmented data quality platform built for enterprises, announced a technology partnership with Databricks, enabling organizations to run Qualytics natively on the Databricks Data Intelligence Platform. The collaboration allows enterprises to ensure their data is accurate, explainable, and AI-ready without moving data outside their Databricks environment.
As enterprises accelerate investments in analytics and AI, data quality has become a critical bottleneck. While Databricks provides a unified foundation for data engineering, analytics, and machine learning, Qualytics addresses a persistent challenge: ensuring the trustworthiness of the data powering those workloads. Together, the two platforms enable proactive data quality management at enterprise scale, shifting teams away from reactive data cleanup toward continuous data control.
By running directly inside the Databricks Data Intelligence Platform, Qualytics eliminates the need for external processing, data replication, or data egress. This native deployment ensures customer data remains within Databricks, supporting strict security, governance, and compliance requirements while accelerating time-to-value for data teams.
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The integrated solution combines Databricks’ scalable lakehouse architecture with Qualytics’ adaptive intelligence for data quality. Qualytics automatically generates and maintains more than 95% of data quality rules, reducing the manual effort typically required to define, update, and manage validation logic. Its deep, row-level anomaly detection enables teams to identify data issues early—before they impact dashboards, machine learning models, or downstream operational systems.
“Databricks gives teams incredible power and flexibility. With Qualytics in the lakehouse, that power is paired with continuous data trust,” said Gorkem Sevinc, Co-Founder and CEO of Qualytics. “Data quality has historically been reactive and slow. Together with Databricks, we’re helping enterprises take control of their data early so AI, analytics, and operational decisions are built on data they can inherently trust.”
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Qualytics offers first-class support for Databricks-native services, including Delta Lake, Lakeflow Jobs, Databricks SQL, and Unity Catalog. All profiling runs, anomaly scans, and rule evaluations execute inside native Databricks Lakeflow Jobs, ensuring seamless alignment with existing lakehouse workflows and avoiding additional architectural complexity.
“Customers want trusted data, and they want it without adding complexity or compromising governance,” said Eric Simmerman, CTO and Co-Founder of Qualytics. “Running inside Databricks means we meet teams exactly where their data lives. They get adaptive intelligence, deep anomaly detection, and enterprise governance without data egress or replication.”
The Qualytics and Databricks integration is available. Existing Databricks customers can deploy Qualytics as a native workload and begin generating automated rules, profiling metadata, and detecting anomalies in a matter of hours, helping ensure analytics and AI initiatives are built on reliable, high-quality data from day one.
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