Last updated at Fri, 03 Nov 2017 17:51:07 GMT

This 3-part series explores the definition and benefits of using log data streams and real-time analytics for some common IT Ops uses cases. To download the complete article, click here.

Many analytics tools focus on just one layer of your application stack. Whether it’s Google Analytics tracking events on your website’s front-end, or a server monitoring tool tracking CPU and memory usage, these tools give you a rather narrow view into a much larger system.

When looking to practice real-time analytics, it’s important that your monitoring tool is tracking every layer of your technology stack – from your application’s front-end to the OS layer.

In Logentries’ article, Using Log Data Streams for Real-Time Analytics, we explore four real-world situations where Real-Time Analytics are necessary. In one scenario, we consider the challenges of identifying an issue’s root cause:

Imagine over the course of several minutes, your popular e-commerce application hasn’t received any orders. Where’s the first place you’d look for a possible issue? You may first check to see if your website is still reachable from a browser. Then, you may check your server logs. Or perhaps you check your APM tool? Or a web analytics tool? Are they all saying the same thing? Or nothing at all? When you notice there aren’t any errors in your code and traffic to your website appears to have remained steady, you decide to investigate your database. Only then, after wasting time investigating other scenarios, do you see your database was improperly configured in the last deployment and has reached its row limit. How many sales have you lost while guessing where to investigate?

Without log-based, real-time analytics, database errors can go undiscovered, often only realized after a period of noticeable inactivity and investigation.

Want to learn more about which situations demand Real-Time Analytics and what to look for in a Real-Time Analytics tool? Download Logentries’ free whitepaper, Using Log Data Streams for Real-Time Analytics.