We built a data lake on AWS for analytics workloads. Architecture: S3 for storage with intelligent tiering, Glue for ETL and schema discovery, Athena for ad-hoc queries, and Redshift Spectrum for complex analytics. Key lessons: partition data properly, use columnar formats (Parquet), and implement data catalog governance. Query costs dropped 80% compared to always-on data warehouse. How do you handle analytics workloads?
Great post! We've been doing this for about 10 months now and the results have been impressive. Our main learning was that security must be built in from the start, not bolted on later. We also discovered that unexpected benefits included better developer experience and faster onboarding. For anyone starting out, I'd recommend cost allocation tagging for accurate showback.
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.
Chiming in with operational experiences we've developed: Monitoring - Datadog APM and logs. Alerting - Opsgenie with escalation policies. Documentation - Notion for team wikis. Training - certification programs. These have helped us maintain low incident count while still moving fast on new features.
One more thing worth mentioning: we discovered several hidden dependencies during the migration.
Additionally, we found that the human side of change management is often harder than the technical implementation.
For context, we're using Istio, Linkerd, and Envoy.
I'd recommend checking out conference talks on YouTube for more details.
The end result was 40% cost savings on infrastructure.
The end result was 90% decrease in manual toil.
One thing I wish I knew earlier: automation should augment human decision-making, not replace it entirely. Would have saved us a lot of time.
The end result was 99.9% availability, up from 99.5%.
For context, we're using Vault, AWS KMS, and SOPS.
Exactly right. What we've observed is the most important factor was starting small and iterating is more effective than big-bang transformations. We initially struggled with scaling issues but found that integration with our incident management system worked well. The ROI has been significant - we've seen 70% improvement.
Additionally, we found that cross-team collaboration is essential for success.
Feel free to reach out if you have more questions - happy to share our runbooks and documentation.
One thing I wish I knew earlier: documentation debt is as dangerous as technical debt. Would have saved us a lot of time.
One thing I wish I knew earlier: automation should augment human decision-making, not replace it entirely. Would have saved us a lot of time.
Feel free to reach out if you have more questions - happy to share our runbooks and documentation.
One more thing worth mentioning: the hardest part was getting buy-in from stakeholders outside engineering.
I'd recommend checking out the official documentation for more details.
Our experience was remarkably similar. The problem: scaling issues. Our initial approach was ad-hoc monitoring but that didn't work because lacked visibility. What actually worked: integration with our incident management system. The key insight was cross-team collaboration is essential for success. Now we're able to deploy with confidence.
I'd recommend checking out relevant blog posts for more details.
One thing I wish I knew earlier: failure modes should be designed for, not discovered in production. Would have saved us a lot of time.
Wanted to contribute some real-world operational insights we've developed: Monitoring - CloudWatch with custom metrics. Alerting - custom Slack integration. Documentation - Confluence with templates. Training - certification programs. These have helped us maintain fast deployments while still moving fast on new features.
Additionally, we found that the human side of change management is often harder than the technical implementation.
The end result was 80% reduction in security vulnerabilities.
We saw this same issue! Symptoms: frequent timeouts. Root cause analysis revealed connection pool exhaustion. Fix: fixed the leak. Prevention measures: chaos engineering. Total time to resolve was 15 minutes but now we have runbooks and monitoring to catch this early.
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.
One thing I wish I knew earlier: the human side of change management is often harder than the technical implementation. Would have saved us a lot of time.
Our team ran into this exact issue recently. The problem: scaling issues. Our initial approach was simple scripts but that didn't work because lacked visibility. What actually worked: real-time dashboards for stakeholder visibility. The key insight was cross-team collaboration is essential for success. Now we're able to detect issues early.
The end result was 80% reduction in security vulnerabilities.
One thing I wish I knew earlier: documentation debt is as dangerous as technical debt. Would have saved us a lot of time.