2026-05 monthly summary for astronomer/airflow: Delivered cost-aware Dataproc lifecycle management by implementing idle and auto-stop TTLs with flexible master/worker policies. Improved cluster configurability and resource efficiency, with test stabilization as part of the rollout. The work reduced idle resource usage, enabling cost savings and faster cluster lifecycle management for Dataproc workloads. Demonstrated proficiency in cloud data processing, cluster lifecycle policies, and test automation.

February 2026 monthly summary for GoogleCloudPlatform/magic-modules: Delivered Instance Flexibility Policy for Dataproc Clusters, enabling a mix of VM shapes for master and worker nodes, enhancing resource allocation, scalability, and potential cost efficiency. Implemented via commit f5a96c0e4be41971b6edfc9210442c51bb19ca16 adding instance_flexibility_policy to master & worker nodes (#16038). No major bugs fixed this month. Impact: improves Dataproc deployment flexibility and strengthens Magic Modules' modeling for Dataproc resources, enabling better Terraform provider coverage and future extensibility. Technologies/skills demonstrated: code contributions, Git collaboration, policy-driven resource provisioning, Terraform/Magic Modules tooling.

Monthly summary for 2025-12: Implemented Dataproc Idle and Auto-Stop Configuration in the GoogleCloudPlatform/magic-modules repository to give users explicit control over cluster lifecycle and cost. The feature adds two new settings, idle_stop_ttl and auto_stop_time, enabling idle-time-based stopping and automatic stopping thresholds to optimize resource usage. The change is tracked under commit 72edcafab87d020451b9e1ad3131c921b0e5efce (Add support for dataproc cluster idle_stop_ttl and auto_stop_time, #15859). No major bugs were reported or fixed this month in relation to this feature. Overall impact includes improved cluster lifecycle automation, potential cost reductions, and a better developer experience for Terraform users interacting with Dataproc resources. Technologies demonstrated include GCP Dataproc, Terraform module development, and codebase maintenance within magic-modules.