The 5 Best Methods for Drawing Insight out of Machine Data

Email     |     Share  
1 | 2 | 3 | 4 | 5 | 6 | 7
Next Next

Request Logs

Data contained in requests made to the server are logged in files. Referrer, remote address, user agent, header data, status codes, response data, etc., can be captured. It usually comes from an application module, library or SDK handles recording the requests and formatting the data being logged.

Why is this important? Almost any data within the stack can be logged – from server, application and client performance to user activities. Log data can be filtered, aggregated and then analyzed via visualization tools to assess most aspects of your product and underlying infrastructure.

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.

 

Related Topics : IBM Looks to Redefine Industry Standard Servers, APC, Brocade, Citrix Systems, Data Center

 
More Slideshows

RKONITGrowthFlexibility0x Building High-Growth IT: 5 Things to Know Now

Five factors IT pros need to consider when strategizing and constructing a computing platform built for growth and flexibility. ...  More >>

DataCtr11-290x195 5 Essential Elements in Building an Agile Data Center

Five key areas that are critical for successful data center modernization efforts include speed, quality of service (QoS), disaster recovery, predictive data analytics and manageability at scale. ...  More >>

cloud47-290x195 Multi-Cloud 101: 7 Things You Need to Know

Many organizations are beginning to consider use of a multi-cloud strategy, in which multiple cloud solutions are leveraged for a more complete and purpose-built cloud architecture. ...  More >>

Subscribe to our Newsletters

Sign up now and get the best business technology insights direct to your inbox.