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

Chapter 8. Designing and Managing Stream Analytics Jobs

In an Enterprise Data Lake scenario, there is a requirement for integrating complex event and streaming architectures with Azure data services resources, such as petabyte scale, big data-Hadoop equivalent file system repositories (Azure Data Lake Store), and a NoSQL multi-modeled, globally-scaled databases such as Azure Cosmos DB, and utilizing serverless configuration streams in a response to events (Azure Functions).

This chapter focuses on:

  • Designing and managing data sources from Azure reference events (Blob storage)
  • Enhancing interactive events with Azure Stream Analytics data sinks such as Azure Data Lake and Azure Cosmos DB
  • How to elevate serverless architecture configuration using Azure Functions

In a complex event processing scenarios, a fully managed event execution engine like Azure Stream Analytics offers very low latency along with support for consistent, historical, or long-term data, and static data such as an event input...