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AWS Cloudwatch Realtime examples / of Cloudwatch Metric and Logs

📊 AWS CloudWatch Metrics — Uses & Examples

AWS CloudWatch Metrics are time-series data points from AWS services. As a Data Engineer, you use them to monitor ETL jobs, detect issues, and automate alerts.

🔹 What Are CloudWatch Metrics?

Metrics are timestamped numerical values that measure resource or application performance — like CPUUtilization = 72%. They include a Namespace (service), Dimensions (filters like instance ID), and a Unit (Percent, Count, Bytes).

💡 Why They Matter for Data Engineers

  • 🧩 Monitor ETL health (run time, success/failure rates)
  • ⚙️ Detect performance bottlenecks (CPU, memory, I/O)
  • 🚨 Trigger alarms & automation (SNS, Lambda, Step Functions)
  • 💰 Plan capacity and optimize cost
🪣 S3 / ⚡ Lambda / 🧠 Glue --> 📈 CloudWatch Metrics --> 📊 Dashboard / ⏰ Alarm / ⚡ EventBridge

📋 Common Metrics Examples

🧱 Service 📊 Metric Name 🎯 Why It Matters
💻 EC2 CPUUtilization Detect high CPU usage during data processing or batch jobs → scale or alert.
📦 S3 NumberOfObjects, BucketSizeBytes Track data lake growth and control storage costs.
🧩 Glue glue.driver.aggregate.numCompletedTasks Monitor job progress and detect stuck tasks.
⚡ Lambda Invocations, Errors, Duration Find failed or slow serverless transformations.
🏢 Redshift CPUUtilization, DatabaseConnections Ensure data warehouse performance under heavy query loads.
🔄 Kinesis GetRecords.IteratorAgeMilliseconds Detect stream consumer lag in real-time pipelines.
🧠 Custom RecordsProcessed, FilesIngested Track ETL KPIs like record count and job runtime.

💬 Example Alarm: CPUUtilization > 80% for 3 of 5 minutes → Send SNS notification or trigger remediation Lambda.

🚀 Real-Time Use Cases

  1. ⏱️ ETL Runtime Alert: Glue job duration metric triggers alarm if runtime exceeds limit → Slack alert via SNS.
  2. 📉 Data Drop Detection: Custom metric RecordsProcessed drops below threshold → Auto-retry pipeline.
  3. 💸 Cost Control: Monitor BucketSizeBytes and alert when storage exceeds budget threshold.

📚 AWS CloudWatch Logs — For Data Engineers & Architects

CloudWatch Logs centralize, monitor, and analyze logs from AWS services and applications in near real-time. They are essential for debugging, observability, compliance, and automation across data platforms.

🔎 Key Capabilities

  • 💾 Log Groups & Streams — organize logs by application, service, or environment.
  • 🔍 Logs Insights — query logs with SQL-like syntax for fast root-cause analysis.
  • 📈 Metric Filters — convert log patterns into numerical metrics and alarms.
  • 🔁 Subscriptions — stream logs to Lambda, Kinesis, or S3 for further processing or archival.

🧩 Use Cases for Data Engineers

Use How Logs Help Example
🔧 Debugging ETL Jobs Full stack traces, Spark executor errors, and job progress appear in Glue/EMR logs. Find "OutOfMemoryError" in Glue logs and identify failing stage.
📊 Data Quality Checks Log validation results and counts, enabling detection of missing or malformed records. Log: "invalid rows=250" → trigger auto-retry or quarantine job.
⏱️ Performance Tuning Measure step durations and latencies to optimize transforms and partitioning. Glue stage X takes 20m — add partition pruning to speed up.
🚨 Alerting Metric filters detect error keywords and raise CloudWatch Alarms/SNS notifications. Filter: /ERROR/ in Lambda logs → Alarm → Slack via SNS.
📚 Auditing & Compliance Retention policies and secure storage for audit trails. Keep pipeline execution logs for 180 days for audit review.

🏛️ Architectural Patterns

Pattern Purpose How to implement
Centralized Log Aggregation Single-pane view across accounts/environments Use centralized Log Group naming (e.g., /prod/data-platform/*), cross-account subscriptions, and CloudWatch Logs Insights dashboards.
Stream & Process Real-time analytics and enrichment Subscribe logs to Kinesis Data Streams → process with Lambda/Firehose → index in OpenSearch or S3.
Error-Driven Automation Auto-remediation for common failures Metric filter on "JobFailed" → CloudWatch Alarm → EventBridge → trigger remediation Lambda.
Cold Storage Archival Cost-effective long-term retention Subscribe logs to S3 via Kinesis Firehose with lifecycle rules (Glacier transition).

🧭 Example Pipeline (Real-Time)

S3 (new file) ➜ Lambda (validate) ➜ Glue (ETL) ➜ Redshift All services ➜ CloudWatch Logs (Log Group per service) ➜ Metric Filters ➜ CloudWatch Alarms / EventBridge / Dashboards

🔧 Tools & Tips

  • 🧠 Use Logs Insights for ad-hoc queries: fields @timestamp, @message | filter @message like /ERROR/ | sort @timestamp desc | limit 20
  • 🔔 Create metric filters for critical error keywords to trigger alarms.
  • 📥 Use subscriptions to send logs to S3 (archive) or OpenSearch (search & viz).
  • 🛡️ Apply IAM policies to control access and set retention for cost control.

✅ Key takeaway: CloudWatch Logs are the "black box" of your data platform — essential for debugging, observability, compliance, and automation. Combine logs with metrics, dashboards, and EventBridge to build resilient, observable pipelines.