Have you ever optimized EMR cost? How would you optimize cost in EMR?

Optimizing Amazon EMR cost involves a combination of right-sizing, instance selection, and workload management. First, choose the appropriate instance family: use memory‑optimized instances (r5, r6g) for Spark jobs, compute‑optimized (c5, c6g) for CPU‑heavy tasks, and storage‑optimized (i3, d2) for I/O‑intensive workloads. Leverage Spot Instances for non‑critical or fault‑tolerant jobs; they can reduce costs by up to 90%. Enable Managed Scaling or Auto Scaling to adjust cluster size based on workload, preventing over‑provisioning. Use EMR Release Labels that support newer Spark versions, which often bring performance improvements that reduce runtime. Turn on EMRFS S3 Sync to avoid unnecessary data copies. Implement data pruning and partitioning to limit the amount of data processed. Schedule jobs during off‑peak hours if using On‑Demand instances, as some regions offer lower spot prices then. Finally, monitor cost and performance with AWS Cost Explorer and CloudWatch; set alerts for budget thresholds. By combining these tactics, you can achieve significant cost savings while maintaining performance.