Great post! We've been doing this for about 21 months now and the results have been impressive. Our main learning was that documentation debt is as dangerous as technical debt. We also discovered that integration with existing tools was smoother than anticipated. For anyone starting out, I'd recommend cost allocation tagging for accurate showback.
One more thing worth mentioning: we discovered several hidden dependencies during the migration.
The end result was 50% reduction in deployment time.
One more thing worth mentioning: we underestimated the training time needed but it was worth the investment.
I'd recommend checking out the community forums for more details.
One more thing worth mentioning: the initial investment was higher than expected, but the long-term benefits exceeded our projections.
Great post! We've been doing this for about 20 months now and the results have been impressive. Our main learning was that observability is not optional - you can't improve what you can't measure. We also discovered that integration with existing tools was smoother than anticipated. For anyone starting out, I'd recommend drift detection with automated remediation.
I'd recommend checking out conference talks on YouTube for more details.
Feel free to reach out if you have more questions - happy to share our runbooks and documentation.
Great points overall! One aspect I'd add is maintenance burden. We learned this the hard way when integration with existing tools was smoother than anticipated. Now we always make sure to document in runbooks. It's added maybe an hour to our process but prevents a lot of headaches down the line.
Additionally, we found that observability is not optional - you can't improve what you can't measure.
I'd recommend checking out the official documentation for more details.
Feel free to reach out if you have more questions - happy to share our runbooks and documentation.
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.
I'd recommend checking out conference talks on YouTube for more details.
The end result was 3x increase in deployment frequency.
Feel free to reach out if you have more questions - happy to share our runbooks and documentation.
Appreciate you laying this out so clearly! I have a few questions: 1) How did you handle security? 2) What was your approach to backup? 3) Did you encounter any issues with latency? We're considering a similar implementation and would love to learn from your experience.
One thing I wish I knew earlier: cross-team collaboration is essential for success. Would have saved us a lot of time.
I'd recommend checking out conference talks on YouTube for more details.
Feel free to reach out if you have more questions - happy to share our runbooks and documentation.
Some tips from our journey: 1) Document as you go 2) Use feature flags 3) Practice incident response 4) Build for failure. Common mistakes to avoid: skipping documentation. Resources that helped us: Google SRE book. The most important thing is collaboration over tools.
Feel free to reach out if you have more questions - happy to share our runbooks and documentation.
For context, we're using Terraform, AWS CDK, and CloudFormation.
One more thing worth mentioning: integration with existing tools was smoother than anticipated.
From an implementation perspective, here are the key points. First, network topology. Second, backup procedures. Third, performance tuning. We spent significant time on automation and it was worth it. Code samples available on our GitHub if anyone wants to take a look. Performance testing showed 2x improvement.
Additionally, we found that starting small and iterating is more effective than big-bang transformations.
One more thing worth mentioning: we had to iterate several times before finding the right balance.
Our data supports this. We found that the most important factor was security must be built in from the start, not bolted on later. We initially struggled with scaling issues but found that automated rollback based on error rate thresholds worked well. The ROI has been significant - we've seen 50% improvement.
For context, we're using Jenkins, GitHub Actions, and Docker.
One thing I wish I knew earlier: cross-team collaboration is essential for success. Would have saved us a lot of time.
Helpful context! As we're evaluating this approach. Could you elaborate on success metrics? Specifically, I'm curious about how you measured success. 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.
Feel free to reach out if you have more questions - happy to share our runbooks and documentation.
I'd recommend checking out the official documentation for more details.