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If you’re not using AI agents to manage your data, then you’re spending way too much time on data management tasks. Think about it: is it a good use of your time spending 10-15 hours (or more) per wee...
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. ......
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......
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......
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......
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......
