What's changed: Created Professional Data Engineer Chapter 5 (Domain 5 "Maintain and automate": resource optimization and automation = cost minimization/Dataproc persistent-vs-job clusters/Cloud Composer DAGs/scheduled queries/BigQuery Editions-reservations; monitoring and fault tolerance = Cloud Monitoring/Logging/BigQuery admin panel, isolate quota/billing/errors, multi-region/zone/replication/failover/idempotent reprocessing/backups-validation).
5.1Optimizing resources and automation
Understand minimizing cost while ensuring resources for business-critical processes, deciding persistent vs job-based Dataproc clusters, automating with Cloud Composer DAGs and repeatable scheduling, and capacity management with BigQuery Editions and reservations.
A data platform is not "build and forget"; keep it running cost-efficiently and automatically. Resource optimization and automation are central to operations.
5.1.1Resource optimization
Minimize cost while ensuring resources for business needs. In Dataproc, choose persistent clusters for steady use or job-based (ephemeral) clusters that start and tear down for occasional batch, avoiding wasteful always-on. Manage BigQuery capacity via on-demand or Editions and reservations for steady use, making cost predictable. Map "occasional batch = job-based cluster" and "steady capacity = BigQuery Editions/reservations."
5.1.2Automation and repeatability
Express job dependencies as DAGs (directed acyclic graphs) in Cloud Composer to schedule/orchestrate repeatably. Simple recurring runs may suffice with BigQuery scheduled queries, but multi-step dependencies suit Cloud Composer. Choose interactive vs batch queries by requirements. Map "repeatable dependent jobs = Cloud Composer DAGs" and "simple recurring aggregation = scheduled queries."
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