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."
Common: requirement → means. E.g., "monitor and alert on data processes" = Cloud Monitoring; "investigate via logs" = Cloud Logging; "see BigQuery jobs/usage" = admin panel; "isolate quota/billing/errors" = troubleshooting; "tolerate failure" = multi-region/zone + replication/failover; "prepare for corruption" = backups/validation/idempotent reprocessing.
Watch the mix-ups: (1) Cloud Monitoring (metrics) and Cloud Logging (logs) differ. (2) Design availability with multi-region/zone and replication/failover. (3) Make reprocessing idempotent; defend corruption/missing data with validation and backups.
5.2.3Section summary
- Monitor = Cloud Monitoring/Logging + BigQuery admin panel; isolate quota/billing/errors
- Fault tolerance = multi-region/zone, restart management, replication/failover
- Defend corruption/missing data with backups, validation, idempotent reprocessing
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Quick check
(just a quick review)Q1. To monitor data-process metrics and alert on threshold breaches, what do you use?
Q2. To see BigQuery jobs, usage, and slot status, what do you use?
Q3. To run data workloads tolerating a whole-region failure, which best fits?
Q4. To ensure reprocessing does not duplicate results, what should you design in?
Q5. A job failed. To investigate the cause in detail via logs, what do you use?
Q6. A job failed with "quota exceeded." What is the most appropriate action?
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