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Stream Analytics with Microsoft Azure
Stream Analytics with Microsoft Azure

Stream Analytics with Microsoft Azure: Real-time data processing for quick insights using Azure Stream Analytics

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Stream Analytics with Microsoft Azure

Introducing Azure Stream Analytics and Key Advantages

Microsoft provides a portfolio of data platform products that includes large volume/scale storage, processing, and orchestration of events. One key advantage offered by these services is that customers can deploy a combination of services and create new offerings that are very specific to their needs.

In this chapter, we will introduce Azure Stream Analytics, get into the configuration, look at the advantages of the Stream Analytics platform and focus on how it will enhance developer productivity and reduce or improve the Total Cost of Ownership (TCO) of building and maintaining a scaling streaming solution.

Let's start working on the material.

Services offered by Microsoft

The following is a very well-known classification of the different type of services offered by Microsoft for on-premises and the cloud. Microsoft Azure provides a full spectrum of services starting with Infrastructure as a Service (IaaS), followed by Platform as a Service (PaaS) and Software as a Service (SaaS) models such as Dynamics CRM, Office 365, and so on:

Service classification model

With IaaS, customers can deploy a virtual machine of their choice without the complexity of buying and managing their own hardware/software; they can quickly scale up and down with demand and pay only for what they use. This will reduce operational expenses of hosting and managing infrastructure.

Azure provides a vast range of PaaS services such as web apps, logic apps, media apps, API apps, mobile apps, Azure Stream Analytics, Azure HDInsight, and Azure Redis...

Introduction to Azure Stream Analytics

Microsoft Azure Stream Analytics falls into the category of PaaS services where the customers don't need to manage the underlying infrastructure. However, they are still responsible and manage an application that builds on the top of PaaS service and more importantly the customer data.

Azure Stream Analytics is a fully managed server-less PaaS service that is built for real-time analytics computations on streaming data. The service can consume from a multitude of sources. Azure will take care of the hosting, scaling, and management of the underlying hardware and software ecosystem. The following are some of the examples of different use cases for Azure Stream Analytics.

When we are designing the solution that involves streaming data, in almost every case, Azure Stream Analytics will be part of a larger solution that the customer was...

Microsoft Cortana Intelligence suite integration

Azure Stream Analytics is a part of the Cortana Intelligence suite. Why is there a need for a streaming solution within the Cortana intelligence suite? Take, for example, number of customers use  Azure Stream Analytics to detect when their servers are down so that they can immediately go and patch or replace those servers to help their site performance and uptime. Data is coming from System Center Operations Manager (SCOM) events and other solutions to this real-time processing solution.

Stream Analytics detects anomalies and triggers system processes to create ServiceNow tickets or show them in live dashboards in real time to monitor the status of the system. In order to do this, you need everything in the Cortana Intelligence Suite (Dashboards, data movement, and so on) to work in concert with Azure Stream Analytics:

Cortana...

Summary

Following is a quick summary of topics we covered in this chapter. We reviewed what is Azure Stream Analytics, understood its key advantages in terms of developer productivity, ease of development and how to reduces total cost of ownership, global compliance certifications, the value of the PaaS based streaming solution to host mission-critical applications and briefly reviewed security.  

Now that we have a basic understanding of Azure Stream Analytics in the next chapter we will explore how to start building a Streaming pipeline, look into deployment, logging and monitoring, and finally review sample use cases to consolidate our learnings.

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Key benefits

  • Analyze your data from various sources using Microsoft Azure Stream Analytics
  • Develop, manage and automate your stream analytics solution with Microsoft Azure
  • A practical guide to real-time event processing and performing analytics on the cloud

Description

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.

Who is this book for?

If you are looking for a resource that teaches you how to process continuous streams of data in real-time, this book is what you need. A basic understanding of the concepts in analytics is all you need to get started with this book

What you will learn

  • • Perform real-time event processing with Azure Stream Analysis
  • • Incorporate the features of Big Data Lambda architecture pattern in real-time data processing
  • • Design a streaming pipeline for storage and batch analysis
  • • Implement data transformation and computation activities over stream of events
  • • Automate your streaming pipeline using Powershell and the .NET SDK
  • • Integrate your streaming pipeline with popular Machine Learning and Predictive Analytics modelling algorithms
  • • Monitor and troubleshoot your Azure Streaming jobs effectively

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Dec 01, 2017
Length: 322 pages
Edition : 1st
Language : English
ISBN-13 : 9781788390620
Vendor :
Microsoft

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Product Details

Publication date : Dec 01, 2017
Length: 322 pages
Edition : 1st
Language : English
ISBN-13 : 9781788390620
Vendor :
Microsoft

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Table of Contents

11 Chapters
Introducing Stream Processing and Real-Time Insights Chevron down icon Chevron up icon
Introducing Azure Stream Analytics and Key Advantages Chevron down icon Chevron up icon
Designing Real-Time Streaming Pipelines Chevron down icon Chevron up icon
Developing Real-Time Event Processing with Azure Streaming Chevron down icon Chevron up icon
Building Using Stream Analytics Query Language Chevron down icon Chevron up icon
How to achieve Seamless Scalability with Automation Chevron down icon Chevron up icon
Integration of Microsoft Business Intelligence and Big Data Chevron down icon Chevron up icon
Designing and Managing Stream Analytics Jobs Chevron down icon Chevron up icon
Optimizing Intelligence in Azure Streaming Chevron down icon Chevron up icon
Understanding Stream Analytics Job Monitoring Chevron down icon Chevron up icon
Use Cases for Real-World Data Streaming Architectures Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.2
(5 Ratings)
5 star 80%
4 star 0%
3 star 0%
2 star 0%
1 star 20%
Zhong Feb 19, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
A very comprehensive book showing how to build effective stream processing pipelines with Azure Stream Analytics service. Would like to see more content on real world use cases, and complex end to end scenarios. Nevertheless, it's an excellent introductory book for stream analytics.
Amazon Verified review Amazon
Jan Mar 02, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I highly recommend this book for anyone who needs to understand the Stream Analytics in Azure and want to be specialized on this area. With so many things to learn, it becomes challenging to find a "structured" path to learn it this book offers a learning path to understand the core things related with Streams Analytics in Azure.
Amazon Verified review Amazon
RR Mar 02, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This is the book if you want to go completely serverless for your near real-time analytics. The book covers everything from event processing to complex event processing. The authors did a great job of not only going deep into stream analytics capabilities in Azure but also covering other relevant Azure services that are used to build end to end real-time analytics solutions.
Amazon Verified review Amazon
CLAUDIA ANGELELLI Jul 04, 2020
Full star icon Full star icon Full star icon Full star icon Full star icon 5
libro eccellente descrive perfettamente scenari di Real-time data processing
Amazon Verified review Amazon
Martin Smith Aug 08, 2019
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1
so far, I've got up to chapter 4.Needs severe editing. The vast majority of paragraphs in the book are very difficult to read as they are not written in coherent flowing English. In far too many places it just becomes word salad and it is difficult to determine what, if any, information the authors are trying to convey.
Amazon Verified review Amazon
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