Title

AWS re:Invent 2023 - Accelerate insights using Amazon CloudWatch Logs ML-powered analytics (COP350)

Summary

Amazon CloudWatch introduced new features for log analysis and management.
Pattern Analysis: Automatically groups and clusters logs into patterns, making it easier to analyze large volumes of log data.
Comparison Analysis: Compares log patterns over different time periods to identify changes, aiding in troubleshooting.
CloudWatch Logs Anomaly Detection: Uses machine learning to monitor logs and surface anomalies, such as new error messages or spikes in log events.
Infrequently Accessed Logs: A new log class that offers a cost-effective solution for storing logs that are not accessed often but are still important for forensic analysis.
Natural Language Querying: A new feature in preview that allows users to generate log and metric queries using natural language, making it easier for those unfamiliar with query languages.
Pricing: Infrequently Accessed Logs class costs 50% less than the standard class for custom log ingestion.

The new features aim to simplify log analysis, especially for large and complex datasets, by leveraging machine learning to automate the process.
Comparison analysis can be particularly useful for identifying issues post-deployment or during outages by comparing logs to a known healthy period.
The Infrequently Accessed Logs class addresses cost concerns by allowing users to store less critical logs at a lower cost while still maintaining accessibility for analysis when needed.
The natural language querying feature democratizes access to log insights, enabling users with varying levels of technical expertise to perform log analysis.
The introduction of these features reflects AWS's commitment to enhancing observability and operational efficiency for their customers.