The reference architecture for real-time event processing with Azure Stream Analytics is intended to provide a generic blueprint for deploying a real-time platform as a service (PaaS) stream-processing solution with Microsoft Azure.
Traditionally, analytics solutions have been based on capabilities such as ETL (extract, transform, load) and data warehousing, where data is stored prior to analysis. Changing requirements, including more rapidly arriving data, are pushing this existing model to the limit. The ability to analyze data within moving streams prior to storage is one solution, and while it is not a new capability, the approach has not been widely adopted across all industry verticals.
Microsoft Azure provides an extensive catalog of analytics technologies that are capable of supporting an array of different solution scenarios and requirements. Selecting which Azure services to deploy for an end-to-end solution can be a challenge given the breadth of offerings. This paper is designed to describe the capabilities and interoperation of the various Azure services that support an event-streaming solution. It also explains some of the scenarios in which customers can benefit from this type of approach.
- Executive Summary
- Introduction to Real-Time Analytics
- Value Proposition of Real-Time Data in Azure
- Common Scenarios for Real-Time Analytics
- Architecture and Components
- Data Sources
- Data-Integration Layer
- Real-time Analytics Layer
- Data Storage Layer
- Presentation / Consumption Layer
Author: Charles Feddersen, Solution Architect, Data Insights Center of Excellence, Microsoft Corporation
Published: January 2015
For further assistance, try our Azure Stream Analytics forum