End to End Monitoring of IoT Stream Analytics

Yes, ingesting CloudWatch metrics into ELK (Elasticsearch, Logstash, Kibana) is a solid approach for building a monitoring framework for your stream processing pipeline.

Key Considerations:

1/ Metric Selection: Carefully select the relevant CloudWatch metrics to monitor. Consider metrics related to CPU utilization, memory usage, network traffic, throughput, latency, and error rates for each component in your pipeline.

2/ Data Ingestion: Use tools like AWS Kinesis Firehose or Logstash to efficiently ingest CloudWatch metrics into ELK.  

3/ Data Enrichment: Consider enriching the ingested data with additional context, such as application names, environment details, or custom tags, to improve correlation and analysis.

4/ Alerting and Notifications: Set up alerts and notifications based on predefined thresholds or anomalies to proactively address issues.

5/ Security: Implement appropriate security measures to protect sensitive data stored in ELK.

Few other Considerations:

1/ Distributed Tracing: For more granular visibility and troubleshooting, consider using distributed tracing tools like AWS X-Ray or Jaeger to track requests across your pipeline.

2/ Custom Metrics: If needed, you can create custom metrics to track specific aspects of your application or pipeline.

3/ Log Aggregation: In addition to metrics, consider aggregating logs from your applications and components into ELK for further analysis and correlation.