Microsoft: Perform advanced analytics on Application Insights data using Jupyter Notebook
To help you leverage your telemetry data and better monitor the behavior of your Azure applications, we are happy to provide a Jupyter Notebook template that extends the power of Application Insights. Instead of making ad hoc queries in the Application Insights portal when an issue arises, you can now write a Jupyter Notebook that routinely queries for telemetry data, performs advanced analytics, and sends the derived data back to Application Insights for monitoring and alerting. You can execute the Jupyter Notebook using Azure WebJob either on a schedule or via webhook.
Through this approach, you can manipulate and analyze your telemetry data beyond the constraints of query language or limit. You can take advantage of the existing alerting system to monitor the newly derived data, rather than raw instrumentation data. The derived data can also be correlated with other metrics for root cause analysis, used to train machine learning models, and much more. In this blog post, you will find a step-by-step guide for operationalizing this template to perform advanced analytics on your telemetry data, as well as an example implementation.
Create a Jupyter Notebook Create a new Notebook or clone the template. While Jupyter supports various programming languages, this blog post focuses on performing advanced analytics in Python 2.7.
Query for telemetry data from Application Insights To query for telemetry data from an Application Insights resource, the Application ID and an API Key are needed. Both can be found in Application Insights portal, on the API Access blade and under Configure.
Read the entire article here, Perform advanced analytics on Application Insights data using Jupyter Notebook | Blog
via the fine folks at Microsoft.