Building on this discussion, I'd highlight security considerations. We learned this the hard way when we underestimated the training time needed but it was worth the investment. Now we always make sure to test regularly. It's added maybe an hour to our process but prevents a lot of headaches down the line.
For context, we're using Terraform, AWS CDK, and CloudFormation.
Additionally, we found that automation should augment human decision-making, not replace it entirely.
Makes sense! For us, the approach varied using Datadog, PagerDuty, and Slack. The main reason was security must be built in from the start, not bolted on later. However, I can see how your method would be better for legacy environments. Have you considered feature flags for gradual rollouts?
One more thing worth mentioning: the hardest part was getting buy-in from stakeholders outside engineering.
Feel free to reach out if you have more questions - happy to share our runbooks and documentation.
Our take on this was slightly different using Datadog, PagerDuty, and Slack. The main reason was automation should augment human decision-making, not replace it entirely. However, I can see how your method would be better for larger teams. Have you considered integration with our incident management system?
Additionally, we found that automation should augment human decision-making, not replace it entirely.
Additionally, we found that starting small and iterating is more effective than big-bang transformations.
Playing devil's advocate here on the team structure. In our environment, we found that Istio, Linkerd, and Envoy worked better because starting small and iterating is more effective than big-bang transformations. That said, context matters a lot - what works for us might not work for everyone. The key is to focus on outcomes.
One thing I wish I knew earlier: starting small and iterating is more effective than big-bang transformations. Would have saved us a lot of time.
Good point! We diverged a bit using Terraform, AWS CDK, and CloudFormation. The main reason was failure modes should be designed for, not discovered in production. However, I can see how your method would be better for fast-moving startups. Have you considered integration with our incident management system?
Feel free to reach out if you have more questions - happy to share our runbooks and documentation.
The end result was 80% reduction in security vulnerabilities.
Great post! We've been doing this for about 21 months now and the results have been impressive. Our main learning was that starting small and iterating is more effective than big-bang transformations. We also discovered that the hardest part was getting buy-in from stakeholders outside engineering. For anyone starting out, I'd recommend real-time dashboards for stakeholder visibility.
I'd recommend checking out relevant blog posts for more details.
I'd recommend checking out the community forums for more details.
Perfect timing! We're currently evaluating this approach. Could you elaborate on the migration process? Specifically, I'm curious about risk mitigation. Also, how long did the initial implementation take? Any gotchas we should watch out for?
Feel free to reach out if you have more questions - happy to share our runbooks and documentation.
Additionally, we found that starting small and iterating is more effective than big-bang transformations.
I'd recommend checking out the community forums for more details.