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· 11 min read
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Logs are essential for diagnosing downtimes, identifying security breaches, and troubleshooting errors. As systems and applications are in operation, they tend to produce a vast array of log files depending on the complexity of the system, the number of applications running, and the configurations in place. In the event of an application or system error, manually gathering and reviewing these logs for troubleshooting purposes can be time-consuming.

· 7 min read
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Datadog and AppDynamics are observability and monitoring tools that provide visibility into the performance and reliability of applications and systems.

Datadog stands out for its comprehensive approach to system performance observability. It provides a broad spectrum of monitoring capabilities, including infrastructure monitoring, application performance monitoring (APM), log management, and real user monitoring (RUM).

· 8 min read
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Datadog and Sentry are monitoring tools but with different use cases. Datadog is tailored towards application performance and full stack observability of systems while Sentry specializes in identifying and reporting application errors.

In this post, I have compared Datadog and Sentry on important features like error monitoring, APM, log management, etc.

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💡 I instrumented a sample Python Application and sent data to both Datadog and Sentry to evaluate my experience. Some takeaways are subjective and based on personal preference.

· 10 min read
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New Relic and Sentry are monitoring tools but with different use cases. New Relic is tailored towards application performance and full stack observability of systems while Sentry specializes in identifying and reporting application errors.

In this post, I have compared New Relic and Sentry on important features like APM, error monitoring, pricing, etc.