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Data analytics metrics can be the answer to optimized app performance
Sponsored by TechTarget Software Quality
How do you measure the success of an application development project? Software developers wrestle with this question during every stage of the process while under the gun to get their project out the door and into the marketplace. The answer lies in all that data -- you know, all the data that pours into your company's troughs from multiple channels and devices. But that wealth of data can be overwhelming and diminish its own value. So even though powerful tools like data analytics metrics are available to developers, rarely do they take advantage of the information necessary to improve the quality of their software projects, reduce project failures and perform proactive maintenance. Metrics "are your safety net," says performance advocate Andreas Grabner. "They tell you if what you are doing actually makes sense, has the right quality and delivers value."
In the first feature of this handbook on data analytics software, Grabner says he's "surprised and shocked" that so few developers are actually aware of the power of data analytics metrics in creating, analyzing and deploying app development projects. In the second feature, test architect Gerie Owen examines the problems associated with big data testing. While testers can't live without data, at times the data can be hard to live with since it's "often the root cause of testing issues." And finally, against the backdrop of the 2016 Summer Olympics in Rio de Janeiro, editor Valerie Silverthorne, in the third feature, talks with Dynatrace field technology evangelist David Jones, who provides tips on optimizing application performance to handle expected and unexpected high-traffic situations.
Table Of Contents
- DevOps metrics answer hard questions
- Getting ready for big data testing
- Tests show performance not so 'Olympic'