Understanding Log Analytics, Log Mining & Anomaly Detection What is Log Analytics

With technologies such as Machine Learning and Deep Neural Networks (DNN), these technologies employ next generation server infrastructure that spans immense Windows and Linux cluster environments. Additionally, for DNNs, these application stacks don’t only involve traditional system resources (CPUs, Memory), but also graphic processing units (GPUs).

Log Analytics supports log search through billions of records, Real-Time Analytics Stack metric collection, and rich custom visualizations across numerous sources. These out of the box features paired with the flexibility of available data sources made Log Analytics a great option to produce visibility & insights by correlating across DNN clusters & components.

With the management of real-time data, the user can use the log file for making decisions.

 But, as the volume of data increases let’s say to gigabytes then, it becomes impossible for the traditional methods to analyze such a huge log file and determine the valid data. By ignoring the log data a huge gap of relevant information will be created.

By combining the useful log data with the Deep Learning it becomes possible to gain the relevant optimum performance and comprehensive operational visibility.

Along with the analysis of log data, there is also need to classify the log file into relevant and irrelevant data. With this approach, time and performance effort could be saved and close to accurate results could be obtained.

This article originally appeared in XenonStack.  To read the full article, click here.