What tools are available for monitoring pipelines in GCP?
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GCP Fundamentals + Advanced Concepts
Real-time Projects with Cloud Data Pipelines
Live Intensive Internship by Industry Experts
Placement-focused Curriculum
Flexible Batches (Weekend & Evening)
Resume Building & Mock Interviews
Hands-on Labs using GCP Console and SDK
What tools are available for monitoring pipelines in GCP?
Google Cloud Platform offers a variety of tools to monitor, debug, and optimize data pipelines across batch and streaming workloads. These tools help Data Engineers gain visibility into pipeline performance, resource utilization, errors, and overall data health.
Cloud Monitoring (formerly Stackdriver Monitoring):
This is the primary tool for tracking metrics, creating dashboards, and setting up alerts for services like Dataflow, Pub/Sub, BigQuery, and more. It provides custom metrics, uptime checks, and alerting policies based on thresholds.
Cloud Logging (formerly Stackdriver Logging):
Used for real-time log analysis across all GCP services. It helps troubleshoot pipeline errors by collecting and querying logs from Dataflow, Pub/Sub, and other GCP resources.
Dataflow Monitoring Interface:
Offers a graphical interface to monitor pipeline stages, job progress, throughput, CPU usage, memory utilization, and errors. You can view pipeline DAGs (Directed Acyclic Graphs), worker statuses, and step-by-step progress.
BigQuery Monitoring:
Through the Query History and Job Information panel, BigQuery offers insights into job execution time, resource consumption, and costs. Integration with Cloud Monitoring allows for real-time alerts on query failures and cost thresholds.
Pub/Sub Monitoring:
Pub/Sub provides subscription metrics (like message backlog, ack rates, and delivery latency), which are visible via Cloud Monitoring dashboards.
Cloud Composer Monitoring:
As a managed Apache Airflow service, Composer includes logs, DAG execution stats, and integration with Cloud Logging for workflow-level observability.
These tools together ensure proactive and reactive monitoring of data pipelines in production environments.
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