The 5 Best Methods for Drawing Insight out of Machine Data

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Application Monitoring

Application monitoring is the information about the performance of an application. The data includes transaction response times, throughput and error rates. A transaction is the work an application did in response to a request from a user and the response time is comprised of any network latency, application processing time, and read/write access to a database or cache.

An agent is installed on each application server or a SDK is used on native applications. Agentless-monitoring solutions exist for applications that support client requests.

Why is this important? Application monitoring enables the engineering team to diagnose performance issues and track errors within an application. In addition to response time, detailed visibility is given into slow code execution via stack traces, and slow database queries are tracked and logged for investigation. Throughput visualization provides insight on how the application performs under various loads and client side monitoring can demonstrate geographical variances for web applications.

The pursuit of data-driven decision making has put tracking, logging and monitoring at the forefront of the minds of product, sales and marketing teams. Engineers are generally familiar with gathering and tracking data to maintain and optimize infrastructure and application performance. However, with the power of data, other business groups are clamoring for the latest tools and instrumentation. Quite often the expense of implementation is undersold as merely placing a tag on a site or adding a library, and doesn’t take into account the additional expense of tracking unique aspects of the application. To complicate matters, there is usually quite a bit of confusion on the types of data being captured by the tools a company already has in place.

In this slideshow, Thomas Overton, from the Sumo Logic Developer Community, offers the five best methods for gaining insight from machine data.


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