Your analytics team operates a Dataplex lake that ingests raw logs into Cloud Storage, processes them with Dataflow streaming jobs, and writes curated tables to BigQuery. They must:
continuously evaluate data quality on each zone asset,
trace lineage from raw objects to curated tables, and
receive automatic email alerts when any Dataflow job throughput falls below a threshold. Which combination of Google Cloud services best satisfies all three monitoring needs?
Instrument pipelines with Stackdriver Trace, collect metrics with the Ops Agent, and trigger alerts by Cloud Scheduler cron jobs.
Catalog assets with Data Catalog, detect anomalies using Vertex AI Model Monitoring, and use Cloud Monitoring only for email alerts.
Query BigQuery INFORMATION_SCHEMA for quality checks, publish lineage metadata to Cloud Logging, and export logs to Pub/Sub to trigger email notifications.
Use Dataplex data quality rules and built-in lineage tracking for requirements 1-2, and configure Cloud Monitoring alert policies on Dataflow metrics with an email notification channel for requirement 3.
Dataplex includes native data quality tasks that run on defined schedules and surface metrics for every managed asset, meeting the continuous data-quality requirement. Dataplex also auto-captures lineage between source objects and downstream BigQuery tables, fulfilling the lineage requirement. Dataflow exports execution metrics such as throughput to Cloud Monitoring; an alerting policy on those metrics combined with an email notification channel delivers proactive alerts. The other options either lack built-in quality and lineage features (BigQuery INFORMATION_SCHEMA, Data Catalog) or rely on unrelated tools (Stackdriver Trace, Ops Agent, Cloud Scheduler) and therefore do not meet all three objectives.
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What are Dataplex data quality rules?
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What metrics can Cloud Monitoring track for Dataflow, and how do alerts work?
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