Book Image

Stream Analytics with Microsoft Azure

By : Krishnaswamy Venkataraman, Anindita Basak, Ryan Murphy, Manpreet Singh
Book Image

Stream Analytics with Microsoft Azure

By: Krishnaswamy Venkataraman, Anindita Basak, Ryan Murphy, Manpreet Singh

Overview of this book

Microsoft Azure is a very popular cloud computing service used by many organizations around the world. Its latest analytics offering, Stream Analytics, allows you to process and get actionable insights from different kinds of data in real-time. This book is your guide to understanding the basics of how Azure Stream Analytics works, and building your own analytics solution using its capabilities. You will start with understanding what Stream Analytics is, and why it is a popular choice for getting real-time insights from data. Then, you will be introduced to Azure Stream Analytics, and see how you can use the tools and functions in Azure to develop your own Streaming Analytics. Over the course of the book, you will be given comparative analytic guidance on using Azure Streaming with other Microsoft Data Platform resources such as Big Data Lambda Architecture integration for real time data analysis and differences of scenarios for architecture designing with Azure HDInsight Hadoop clusters with Storm or Stream Analytics. The book also shows you how you can manage, monitor, and scale your solution for optimal performance. By the end of this book, you will be well-versed in using Azure Stream Analytics to develop an efficient analytics solution that can work with any type of data.
Table of Contents (18 chapters)
Title Page
About the Authors
About the Reviewers
Customer Feedback

Logging of diagnostics logs

Azure diagnostics logs are another great troubleshooting mechanism to get information about the operation of an Azure resource—a Stream Analytics job in our case. Let's understand how the information provided by diagnostics logs can help you to troubleshoot issues faster, in case of an unexpected event. You can use diagnostics logs to collect the following categories of data:

  • Execution: Log events occurring at runtime, that is, during job execution
  • Authoring: Capturing job authoring events, such as job creation/modification
  • All Metrics: Log metrics emitted by a Stream Analytics job

When it comes to storage and consumption of diagnostics logs, you have the following three options at hand:

  • Archive to a storage account: You can choose to store the diagnostics logs on a storage account for later analysis, or for auditing purposes. When a storage account is chosen as an option, you can optionally specify a retention period (in days) for the logs. A retention period configuration...