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).
5.2Monitoring, troubleshooting, and fault tolerance
Understand observability of data processes (Cloud Monitoring, Cloud Logging, BigQuery admin panel), troubleshooting errors/billing/quotas, and fault tolerance (running in multiple regions/zones, preparing for corruption/missing data, replication and failover).
Operate data workloads to monitor and catch issues and to limit impact when failures occur. Observability and fault tolerance underpin reliability.
5.2.1Monitoring and troubleshooting
Observe data processes with Cloud Monitoring (metrics/alerts), Cloud Logging (logs), and the BigQuery admin panel. Monitor planned usage and catch unexpected increases. Troubleshoot by isolating error messages, billing issues, and quota overruns. Manage workloads such as jobs, queries, and reservations (compute capacity). Map "monitor data processes = Cloud Monitoring/Logging + BigQuery admin panel" and "isolate quota/billing/errors."
5.2.2Fault tolerance and mitigating impact
Design systems to be fault-tolerant: manage restarts, run jobs across multiple regions/zones, and prepare for corruption and missing data (backups, idempotent reprocessing, validation). Raise database availability with replication and failover (Cloud SQL HA, Redis clusters, etc.). Map "tolerate failure = multi-region/zone + replication/failover" and "corruption defense = backups/validation/idempotent reprocessing."
Continue reading — free sign-up
You're reading the free preview. Sign up free to read this section in full, plus every chapter (including 4+) and all questions.