In Q3 2024, our 12-person platform team slashed log ingestion spend by 35% in 90 days, moving from a brittle Elasticsearch-based pipeline to a tuned Vector 0.30 and Loki 3.0 stack—without losing a single log or breaking our 99.95% SLA. GameStop makes $55.5B takeover offer for eBay (279 points) Talking to 35 Strangers at the Gym (144 points) Newton's law of gravity passes its biggest test (15
We Cut Compliance Costs by 40% Using Pulumi 3.140 and Chef 18 for Multi-Cloud AWS and GCP Modern multi-cloud environments offer unmatched flexibility, but they also introduce complex compliance challenges. For our team managing hybrid infrastructure across AWS and GCP, manual policy enforcement and fragmented tooling were driving up compliance costs by 22% year-over-year. By integrating Pulumi 3
In Q3 2024, our 12-person platform engineering team reduced confirmed security incidents by 41.7% (from 72 to 42 per quarter) after rolling out Trivy 0.50 for pre-deployment scanning and Falco 0.40 for runtime detection across 142 production microservices. We didn’t rewrite our CI/CD pipeline, we didn’t hire a dedicated security team, and we didn’t spend a dime on enterprise security tools. Here’s
If you’ve been around data engineering long enough, you’ve probably heard these terms thrown around in meetings: “Just dump it in the data lake” “We’ll expose it through the warehouse” “That goes into the mart” “We’re moving to a lakehouse architecture” And honestly… it can sound like four different ways of saying the same thing. They’re not. Each one solves a slightly different problem in the dat