Data Quality

#data-quality

How to Get Started with Data Quality as Code in Your Pipelines

In Part 1, we explored why traditional data quality testing happens too late—after bad data has already reached production. Data Quality as Code shifts validation left, letting you catch issues during transformation before they propagate downstream. ...

Introducing Data Quality as Code: Validate Data Before It Lands, Not After

Every data team knows the scenario: By the time quality tests catch errors, bad data has already reached production and hit user dashboards and business reports. The problem isn't that organizations don't test their data; it's that they test it too l...

Announcing Collate 1.11

We're excited to announce Collate 1.11, the latest release of our managed OpenMetadata service—and a significant leap forward in AI-powered semantic intelligence. This release introduces: AskCollate - our conversational AI assistant that brings natu...

End the 2 AM Pipeline Fixes: Collate Collaborative, Actionable Data Contracts Stop Breaks Before They Start

The broken dashboard at 2 AM. The failing ML model. The frantic Slack message: "Why did this column disappear?" Data teams everywhere face these crises because organizations often manage data reactively, allowing schema changes to occur without warni...

Collate AI AutoPilot Supercharges Data Team Productivity

Let’s be real, data teams are drowning in manual work. On average, they’re spending around 70% of their time just trying to keep things in order — documenting datasets, tagging them with the right classifications, and writing tests to maintain qualit...

Announcing Collate 1.7

We are excited to announce Collate Release 1.7, a transformative update for our managed OpenMetadata services. This new release uses AI and intelligent automation to tackle the challenges of tedious data management head-on. At the heart of this relea...