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Stream Analytics with Microsoft Azure

By : Murphy, Singh

4.2 (5)

Buy this Book

Stream Analytics with Microsoft Azure

4.2 (5)

By: Murphy, Singh

Buy this Book

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.

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

Introducing Stream Processing and Real-Time Insights

Understanding stream processing

Understanding queues, Pub/Sub, and events

What is an event?

Summary

Introducing Azure Stream Analytics and Key Advantages

Services offered by Microsoft

Introduction to Azure Stream Analytics

Microsoft Cortana Intelligence suite integration

Summary

Designing Real-Time Streaming Pipelines

Differencing stream processing and batch processing

Canonical Azure architecture

Summary

Developing Real-Time Event Processing with Azure Streaming

Stream Analytics tools for Visual Studio

Defining a Stream Analytics query for Vehicle Telemetry job analysis using Stream Analytics tools

Implementation of an Azure Stream Analytics job using the Azure portal

Summary

Building Using Stream Analytics Query Language

Built-in functions

Query language elements

Windowing

Time management and event delivery guarantees

Summary

How to achieve Seamless Scalability with Automation

Understanding parts of a Stream Analytics job definition (input, output, reference data, and job)

How to scale queries using Streaming units and partitions

Sample use case

Configuring SU using Azure portal

Out of order and late-arriving events

Summary

Integration of Microsoft Business Intelligence and Big Data

What is Big Data Lambda Architecture?

Moving to the streaming-based data solution pattern

Evolution of Kappa Architecture and benefits

Comparison between Azure Stream Analytics and Azure HDInsight Storm

Designing data processing pipeline of an interactive visual dashboard through Stream Analytics and Power BI

Summary

Designing and Managing Stream Analytics Jobs

Reference data streams with Azure Stream Analytics

Summary

Optimizing Intelligence in Azure Streaming

Integration of JavaScript user-defined functions using Azure Stream Analytics

Summary

Understanding Stream Analytics Job Monitoring

Troubleshooting with job metrics

Visual monitoring of job diagram

Logging of diagnostics logs

Configuring job alerts

Viewing resource health information with Azure resource health

Exploring different monitoring experiences

Summary

Use Cases for Real-World Data Streaming Architectures

Solution architecture design and Proof-of-Concept implementation of social media sentiment analytics using Twitter and a sentiment analytics dashboard

Summary

Customer Reviews

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Understanding parts of a Stream Analytics job definition (input, output, reference data, and job)

Stream Analytics job definition includes inputs, a query, and output. We will start the topic with a brief introduction to input, output and query. We will look into each of the components in detail as follows:

Input: Data connections to Stream Analytics from a given event source is called an input. For instance, Stream Analytics can connect directly to Azure Event Hubs, IoT Hub, and Blob storage to receive events. Do note that the Azure resource mentioned can be from the same or different subscriptions. Stream Analytics can handle high event throughput of up to 1 GB per second.
Reference data: In addition to input, stream sets also support the second type of input, called auxiliary input (Reference data), to include a reference or static data.
Output: Results sets from Stream Analytics...