How Platform Engineering Boosts Team Productivity

A Platform Engineering VP shared some eye-opening numbers with me yesterday: VP: "I've been trying to quantify our environment costs. We have 200 developers, each making ~5 PRs a week." Me: "How long does it take to test each change?" VP: "Average wait time for a test environment is 45 minutes. Plus another hour to run tests. Sometimes up to 3 hours if there are conflicts." *does quick math* "That's about 2000 engineering hours every week just waiting for environments and test results." VP: "Exactly. At $150/hr fully loaded engineering cost, we're burning $300K every week. But duplicating environments for faster testing would cost even more in infrastructure." This is when I shared how modern service mesh architectures are changing this equation: Instead of duplicating infrastructure, you can create instant test environments by isolating at the request level using Istio/Linkerd. Each developer gets their own "slice" of the environment through smart request routing. The numbers got interesting: - Infrastructure costs: Down 90% (sharing resources vs duplicating) - Wait times: From 45 mins to 2 mins - Test completion: From hours to minutes - Time to debug issues: Cut in half The VP's response: "So we can give every developer instant environments AND reduce costs?" This is why I'm excited about modern cloud native architectures. The ROI isn't incremental - it's transformative. Real question: How are other platform teams measuring the cost of slow testing cycles? Would love to hear your metrics. #platformengineering #devops #microservices #productivity

For T-Mobile, shifting from a focus on developer experience to developer productivity helped their Platform team become more successful. They previously saw developer experience as being about making developers happy. But when they shifted to focusing on productivity and how to reduce friction, they started getting more executive support and driving more meaningful results. In a conversation with Chris Chandler, we get the story of how they made this shift and some of the major projects they’ve delivered since. We discuss: - What drove the transition to focus on developer productivity - What they did to get executives to care about developer productivity initiatives - Why they created and patented Starter Kits and built their Dev Center platform - How they secured funding to build a developer platform - The pros and cons of how they built their platform compared to others like Backstage Listen to our conversation:  - DX: https://lnkd.in/gDVejD64 - Spotify: https://lnkd.in/gi32iUaj - Apple: https://lnkd.in/g6pNdpyj - YouTube: https://lnkd.in/gT2cXnSY

The Engineering Effectiveness Handbook https://ee-handbook.io is one that every software engineering organization should make mandatory to read as part of the onboarding process The Engineering Effectiveness Handbook offers comprehensive guidance for engineering leaders aiming to enhance team productivity Here are three key insights from the handbook: 1. Prioritizing Uninterrupted ‘Maker Time’: The handbook emphasizes the importance of allocating substantial, uninterrupted time blocks—referred to as “Maker Time”—for engineers to achieve optimal productivity. It suggests that periods of at least two hours are essential for engineers to enter a flow state, minimizing context switching caused by interruptions like meetings or alerts. Strategies to increase Maker Time include clustering meetings and reducing unnecessary interruptions 2. Optimizing Feedback Loops: Efficient feedback loops are crucial for rapid development cycles. The handbook discusses the significance of minimizing latency in processes such as code reviews, continuous integration, and deployments. For instance, it highlights that prolonged code review times can hinder progress and suggests monitoring metrics like ‘Time to First Action’ to identify bottlenecks. Implementing best practices in code reviews and CI/CD pipelines can lead to faster iterations and higher code quality 3. Implementing Meaningful Metrics: The handbook advocates for the use of actionable metrics to assess and improve engineering effectiveness. It cautions against metrics that can be gamed or that focus solely on individual performance, such as lines of code written. Instead, it recommends metrics that identify bottlenecks and inefficiencies, like lead time for changes, deployment frequency, and change failure rate. These metrics provide insights into team performance and areas needing attention By focusing on these areas, engineering leaders can create an environment that fosters productivity, reduces friction in development processes, and leads to the delivery of high-quality software #DORA #SPACE #PlatformEngineering