AWS CloudWatch is a monitoring and observability service offered by Amazon Web Services (AWS) that provides data and actionable insights to monitor applications, respond to system-wide performance changes and optimize resource utilization.
CloudWatch collects monitoring and operational data in logs, metrics, and events, providing a detailed view of resources and applications running on AWS and on-premises servers. It lets you set alarms, visualize logs and metrics side by side, take automated actions, troubleshoot issues, and discover insights to keep your applications running smoothly.
CloudWatch’s versatility makes it a useful tool for anyone seeking to streamline the performance and health of their AWS resources and applications.
AWS CloudTrail is a service that records actions taken by a user, role, or AWS service. It simplifies compliance audits, security analysis, and operational troubleshooting by providing a history of AWS API calls for your account. These include actions taken through the AWS Management Console, AWS SDKs, command-line tools, and other AWS services.
Cloud Trail collects data such as the API caller’s identity, time, and source IP address. This allows for detailed auditing and analysis of the AWS environment, making it easier to detect unusual activity and troubleshoot operational issues.
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Here are some of the main differences between CloudWatch and CloudTrail
CloudWatch focuses on the performance and health monitoring of AWS resources and applications. It collects and tracks metrics, logs, and event data, providing insights to improve performance and availability. CloudWatch’s functionality is geared towards operational health, with features for dashboard creation, alarms, anomaly detection, and insights.
CloudTrail focuses on auditing and logging API activity within the AWS environment. It provides a detailed record of who did what on AWS, including API calls made via various interfaces. CloudTrail is geared towards security and compliance monitoring, offering insights into account activity and resource changes.
CloudWatch offers near-real-time monitoring with metrics updated in one-minute intervals. This allows for prompt detection and response to issues affecting performance or availability. CloudWatch Logs can stream data continuously, providing immediate access to log data for analysis and alerting.
CloudTrail records API activities with a slightly different timing model. Logs are typically delivered within a few minutes of an API call, making it suitable for auditing and historical analysis rather than real-time monitoring. CloudTrail data focuses more on forensic and compliance needs than operational troubleshooting.
CloudWatch integrates closely with various AWS services to automatically collect and aggregate data. It supports various AWS resources and third-party tools for extended monitoring capabilities. CloudWatch also provides features for analyzing log and metric data directly within the service, enabling users to identify trends and patterns.
CloudTrail’s integration with AWS services centers around logging API actions. While it does not directly offer analysis tools, the logs can be exported to other services like Amazon S3 for storage or AWS Athena for querying.
Here are some examples of how CloudWatch can be used.
CloudWatch allows for the centralized collection and management of log data from various sources. Aggregating log files across multiple AWS resources and applications simplifies log analysis and monitoring, making detecting patterns, identifying issues, and understanding system-wide performance easier.
This centralized approach streamlines troubleshooting by enabling quick access to relevant log data. It supports real-time monitoring and alerting, helping teams respond to irregularities without sifting through logs on individual servers or services.
Learn more in our detailed guide to Cloudwatch logs.
CloudWatch enables the ability to track and monitor user requests across application stacks. By collecting and analyzing metrics and logs, CloudWatch provides insights into request patterns, response times, and potential bottlenecks, improving the ability to diagnose and debug issues within the application.
This visibility helps optimize application performance and enhance user experience. By understanding how user requests flow through the system and where delays or errors occur, teams can make targeted improvements to enhance reliability and speed.
CloudWatch enables the creation of thresholds and alarms based on specific metrics, which can trigger notifications or automated actions. This feature is critical for maintaining application performance and availability, alerting teams to potential issues before they impact users.
Additionally, CloudWatch can trigger auto-scaling actions, adjusting resource allocation automatically based on demand. This ensures applications maintain optimal performance during peak times without manual intervention.
While CloudWatch is a useful platform, there are some important limitations to be aware of. Users on the G2 platform reported these limitations.
Getting used to CloudWatch can be time-consuming, requiring a substantial learning curve to harness its capabilities fully. New users, especially those with limited AWS experience, may struggle to navigate and utilize the service effectively. The interface and functionality require a good understanding of AWS ecosystems and monitoring principles.
The service does not support aggregating alarms, which can lead to alert fatigue, and is limited to text-based alarms. Users report these alarms are not always reliable. Users looking for more nuanced or composite alarm configurations might find CloudWatch’s offerings inadequate, leading to potential gaps in monitoring and alerting.
CloudWatch’s log stream can experience delays, with events sometimes not being reported until 30 minutes after they occur. This delay can introduce challenges in real-time monitoring and event management. These delays can be a significant issue for operations requiring immediate data or for those using CloudWatch for critical real-time analysis.
Here are some examples of how CloudTrail can be used.
CloudTrail promotes security and regulatory compliance by recording and storing account activity and API usage. This log data is valuable for auditing, enabling organizations to prove compliance with governance standards and policies.
CloudTrail helps organizations detect potential security threats and unauthorized access by monitoring and analysing user activity and API calls.
In the event of a security incident, CloudTrail provides the detailed historical data needed for forensic analysis. Tracking user actions and resource changes over time it enables investigators to reconstruct events, identify the cause of the breach, and implement tighter security measures.
This deep visibility into account activity is critical for understanding the scope and impact of an incident, supporting effective response strategies, and mitigating future risks.
CloudTrail offers clear, auditable records of changes and activities within AWS accounts. This helps teams identify recent changes that might have caused operational issues. The comprehensive logging of user actions and API calls facilitates the diagnosis and resolution of problems, improving system stability and reducing downtime.
Here are some limitations of CloudTrail, reported by users on the G2 platform.
As applications grow, managing logs within AWS CloudTrail can become increasingly costly. Log storage expenses, especially when not meticulously managed, can rapidly escalate, making them a significant financial consideration for large or log-heavy projects.
The default log retention period in CloudTrail is 90 days, which might not meet certain organizations’ compliance requirements. Adjusting these settings for longer retention can add complexity and further costs. This requires careful planning and management to align with legal and operational standards without disproportionately impacting budgets.
CloudTrail can experience latency in capturing requests, which is particularly problematic during real-time debugging with a support team. High usage volumes may exacerbate the delay in event capture, leading to increased latency. This makes CloudTrail less suitable for immediate troubleshooting in high-demand environments.
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Reduce troubleshooting time by over 80%: Lumigo automatically enriches traces with complete in-context request and response payloads and correlates them to the relevant logs and metrics. This enables developers to view logs in the context of the associated traces while seamlessly navigating from logs to traces and vice versa. Lumigo brings all your troubleshooting data into a single, correlated dashboard view.
