In software delivery circles, it’s still common to hear, “Performance testing can wait until just before release.” But that thinking is increasingly out of step with the realities of modern development. Continuous performance testing (CPT) is no longer a nice-to-have for high-maturity organizations – it’s becoming the baseline for any team serious about reliability and user experience. Treating CPT as optional isn’t a conservative move; it’s a risk.
Two engineers used AI coding agents to design, build, and ship a production-ready status page — here's what worked, what didn't, and what we'd do differently.
Most enterprise AI projects stall when teams try to move experiments into production—where costs, governance, data security, and scale all get real. In this demo, see how Cloudera AI Inference helps turn foundation models into secure, governed, production-ready AI services. You’ll learn how to: Chapters: Subscribe to stay ahead of the curve with the latest in data strategy, open architectures, and enterprise AI innovations.
From our CodePush webinar: a practical walkthrough of setting up and managing CodePush for over-the-air (OTA) updates on Bitrise using a React Native application. Staff Solutions Engineer Atanas Chanev shows you how updates are handled by pushing a direct update via the command-line interface, promoting a staging release to production, and executing an emergency rollback to a previous version using the API.
Engineering leadership faces a persistent dilemma: accelerating release velocity or protecting platform stability. As microservice architectures scale and daily commit volumes grow, test suite execution times stretch from minutes into hours. Flaky scripts and unstable UI locators break build pipelines, forcing senior engineers to spend sprint cycles debugging false positives.
AI is writing more of your codebase every quarter. A new Sauce Labs report surveying 400 U.S. executives and engineering leaders shows most organizations still can't verify it's safe to ship, and the incidents are piling up.
Your data pipeline breaks at 2 AM. Again. By morning, corrupted data has cascaded through dashboards, reports sit empty, and your team spends half the day tracking down root causes instead of building features. This scenario plays out across organizations daily. Data engineers spend 44% of their time firefighting pipeline failures rather than delivering value.