Our proprietary Data Quality Engine scans 1.17 billion data points in 23 seconds and turns invisible data issues into a managed, shrinking list your teams can actually work.
DQE™ is not a rules checklist bolted onto a warehouse. It is a production engine that scans, scores, logs, and closes data quality violations across the entire estate.
Rules spanning all data quality dimensions: completeness, accuracy, consistency, timeliness, uniqueness, and validity, running in one engine instead of six point tools.
Validation that reaches across domains, with custom scoring that exceeds the native data quality capabilities of Snowflake and Databricks.
DQE™ identifies violations automatically and closes them the moment they are remediated at the source, so the open list reflects reality, not history.
Custom Power BI monitoring reports give leaders and stewards a live view of violations, trends, and closures without reading raw logs.
A Massively Parallel Processing (MPP) architecture is how DQE™ scans 1.17 billion data points in 23 seconds, fast enough to run constantly, not quarterly.
Built on Databricks and Snowflake, deployable to any environment. DQE™ meets your platform where it is instead of forcing a migration.
DQE™ was forged inside a live engagement at a $40B aerospace and defense supplier. Here is what its first scan surfaced, and what happened next.
On its first scan, DQE™ flagged more than 13,000 previously invisible data quality violations, issues no existing tool had surfaced. Sustained remediation then drove the daily count to under 100, a 99.4% reduction.
Detailed violation logging let teams focus on high-confidence, actionable resolution. And the data-backed insights gave the client negotiating leverage with suppliers and customers.
A data quality engine is software that continuously scans enterprise data against defined rules and reports every violation it finds. DQE™, our proprietary engine, runs 200+ rules across completeness, accuracy, consistency, timeliness, uniqueness, and validity, scanning 1.17 billion data points in 23 seconds and logging each violation in detail so teams can act on it.
DQE™ performs cross-domain validation and custom scoring that exceed the native data quality capabilities of Snowflake and Databricks. It also identifies and closes violations automatically once they are remediated at the source, and ships with custom Power BI monitoring reports, so leaders watch the violation count shrink instead of reading raw logs.
All of them. DQE™ runs 200+ rules spanning completeness, accuracy, consistency, timeliness, uniqueness, and validity, the full set of data quality dimensions, in a single engine.
Yes. DQE™ is platform-agnostic. It is built on Databricks and Snowflake and is deployable to any environment, with a Massively Parallel Processing (MPP) architecture that keeps scans fast at enterprise scale.
Most organizations discover their data quality problem in a forecast. DQE™ finds it first, logs it in detail, and drives it down. Tell us about your platform and we'll show you what a first scan looks like.
