Data & Analytics
DataOps vendors push AI inside pipelines, not just at endpoints
DataOps.live argues AI should validate data quality during ETL, not after. The pitch: catch pipeline failures earlier. The reality: 80% of data engineer time goes to routine ops, making automation targets obvious. What's less clear is whether enterprises want another layer of complexity before production.