Stream Analytics with Microsoft Azure 1st Edition by Anindita Basak, Krishna Venkataraman, Ryan Murphy, Manpreet Singh ISBN 1788395905 9781788395908 by Basak, Anindita;venkataraman, Krishna;singh, Manpreet 9781788395908, 1788395905 instant download after payment.

Stream Analytics with Microsoft Azure 1st Edition by Anindita Basak, Krishna Venkataraman, Ryan Murphy, Manpreet Singh - Ebook PDF Instant Download/Delivery: 1788395905, 9781788395908
Full download Stream Analytics with Microsoft Azure 1st Edition after payment

Product details:

ISBN 10: 1788395905 
ISBN 13: 9781788395908
Author: Anindita Basak, Krishna Venkataraman, Ryan Murphy, Manpreet Singh

Develop and manage effective real-time streaming solutions by leveraging the power of Microsoft Azure

Key Features
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
Book 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.
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
Who this book is 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

Stream Analytics with Microsoft Azure 1st Table of contents:

1. What this book covers
2. What you need for this book
3. Who this book is for
4. Conventions
5. Reader feedback
6. Customer support
7. Downloading the example code
8. Downloading the color images of this book
9. Errata
10. Piracy
11. Questions
12. Introducing Stream Processing and Real-Time Insights
13. Understanding stream processing
14. Understanding queues, Pub/Sub, and events
15. Queues
16. Publish and Subscribe model
17. Real-world implementations of the Publish/Subscribe model
18. Azure implementation of queues and Publish/Subscribe models
19. What is an event?
20. Event streaming
21. Event correlation
22. Azure implementation of event processing
23. Architectural components of Event Hubs
24. Simple event processing 
25. Event stream processing
26. Complex event processing 
27. Summary
28. Introducing Azure Stream Analytics and Key Advantages
29. Services offered by Microsoft
30. Introduction to Azure Stream Analytics
31. Configuration of Azure Stream Analytics
32. Key advantages of Azure Stream Analytics
33. Security
34. Programmer productivity
35. Declarative SQL constructs
36. Built-in temporal semantics
37. Lowest total cost of ownership
38. Mission-critical and enterprise-less scalability and availability
39. Global compliance
40. Microsoft Cortana Intelligence suite integration
41. Azure IoT integration
42. Summary
43. Designing Real-Time Streaming Pipelines
44. Differencing stream processing and batch processing
45. Logical flow of processing
46. Out of order and late arrival of data
47. Session grouping and windowing challenges 
48. Message consistency  
49. Fault tolerance, recovery, and storage
50. Source
51. Communication and collection
52. Ingest, queue, and transform
53. Hot path
54. Cold path
55. Data retention
56. Presentation and action
57. Canonical Azure architecture
58. Summary
59. Developing Real-Time Event Processing with Azure Streaming
60. Stream Analytics tools for Visual Studio
61. Prerequisites for the installation of Stream Analytics tools
62. Development of a Stream Analytics job using Visual Studio
63. Defining a Stream Analytics query for Vehicle Telemetry job analysis using Stream Analytics tools
64. Query to define Vehicle Telemetry (Connected Car) engine health status and pollution index over cities
65. Testing Stream Analytics queries locally or in the cloud
66. Stream Analytics job configuration parameter settings in Visual Studio
67. Implementation of an Azure Stream Analytics job using the Azure portal
68. Provisioning for an Azure Stream Analytics job using the Azure Resource Manager template
69. Azure ARM Template - Infrastructure as code
70. Getting started with provisioning Azure Stream Analytics job using the ARM template
71. Deployment and validation of the Stream Analytics ARM template to Azure Resource Group
72. Configuration of the Azure Streaming job with different input data sources and output data sinks
73. Data input types-data stream and reference data
74. Data Stream inputs
75. Reference data
76. Job topology output data sinks of Stream Analytics
77. Summary
78. Building Using Stream Analytics Query Language
79. Built-in functions
80. Scalar functions
81. Aggregate and analytic functions
82. Array functions
83. Other functions
84. Data types and formats
85. Complex types
86. Query language elements
87. Windowing
88. Tumbling windows
89. Hopping windows
90. Sliding windows
91. Time management and event delivery guarantees
92. Summary
93. How to achieve Seamless Scalability with Automation
94. Understanding parts of a Stream Analytics job definition (input, output, reference data, and job)
95. Deployment of Azure Stream Analytics using ARM template 
96. Configuring input
97. Configuring output
98. Building the sample test code
99. How to scale queries using Streaming units and partitions
100. Application and Arrival Time
101. Partitions
102. Input source
103.  Output source
104. Embarrassingly parallel jobs and Not embarrassingly parallel jobs
105. Sample use case 
106. Configuring SU using Azure portal
107. Out of order and late-arriving events
108. Summary
109. Integration of Microsoft Business Intelligence and Big Data
110. What is Big Data Lambda Architecture?
111. Concepts of batch processing and stream processing in data analytics
112. Specifications for slow/cold path of data - batch data processing
113. Moving to the streaming-based data solution pattern
114. Evolution of Kappa Architecture and benefits 
115. Comparison between Azure Stream Analytics and Azure HDInsight Storm
116. Designing data processing pipeline of an interactive visual dashboard through Stream Analytics and Power BI
117. Integrating Power BI as an output job connector for Stream Analytics
118. Summary
119. Designing and Managing Stream Analytics Jobs
120. Reference data streams with Azure Stream Analytics
121. Configuration of Reference data for Azure Stream Analytics jobs
122. Integrating a reference data stream as job topology input for an Azure Stream Analytics job
123. Stream Analytics query configuration for Reference Data join
124. Refresh schedule of a reference data stream
125. Configuration of output data sinks for Azure Stream Analytics with Azure Data Lake Store 
126. Configuring Azure Data Lake Store as an output data sink of Stream Analytics
127. Configuring Azure Data Lake Store as an output sink of Stream Analytics jobs
128. Configuring Azure Cosmos DB as an output data sink for Azure Stream Analytics 
129. Features of Azure Cosmos DB for configuring output sinks of Azure Stream Analytics
130. Configuring Azure Cosmos DB integrated with Azure Stream Analytics as an output sink
131. Stream Analytics job output to Azure Function Apps as Serverless Architecture 
132. Provisioning steps to an Azure Function 
133. Configuring an Azure function as a serverless architecture model integrated with Stream Analytics job output
134. Summary 
135. Optimizing Intelligence in Azure Streaming
136. Integration of JavaScript user-defined functions using Azure Stream Analytics
137. Adding JavaScript UDF with a Stream Analytics job
138. Stream Analytics and JavaScript data type conversions
139. Integrating intelligent Azure machine learning algorithms with Stream Analytics function
140. Data pipeline Streaming application building concepts using Azure .NET Management SDK
141. Implementation steps of Azure Stream Analytics jobs using .NET management SDK
142. Summary
143. Understanding Stream Analytics Job Monitoring
144. Troubleshooting with job metrics
145. Visual monitoring of job diagram
146. Logging of diagnostics logs
147. Enabling diagnostics logs
148. Exploring the logs sent to the storage account
149. Configuring job alerts
150. Viewing resource health information with Azure resource health
151. Exploring different monitoring experiences
152. Building a monitoring dashboard
153. Summary
154. Use Cases for Real-World Data Streaming Architectures
155. Solution architecture design and Proof-of-Concept implementation of social media sentiment analytics using Twitter and a sentiment analytics dashboard
156. Definition of sentiment analytics
157. Prerequisites required for the implementation of Twitter sentiment analytics PoC
158. Steps for implementation of Twitter sentiment analytics
159. Remote monitoring analytics using Azure IoT Suite 
160. Provisioning of remote device monitoring analytics using Azure IoT Suite
161. Implementation of a connected factory use case using Azure IoT Suite
162. Connected factory solution with Azure IoT Suite
163. Real-world telecom fraud detection analytics using Azure Stream Analytics and Cortana Intelligence Gallery with interactive visuals from Microsoft Power BI
164. Implementation steps of fraud detection analytics using Azure Stream Analytics
165. Steps for building the fraud detection analytics sol

People also search for Stream Analytics with Microsoft Azure 1st:

    
stream analytics microsoft
    
azure stream analytics examples
    
azure stream analytics with
    
azure stream analytics overview
    
stream analytics query
    
azure stream analytics uses

Tags: Anindita Basak, Krishna Venkataraman, Ryan Murphy, Manpreet Singh, Analytics, Microsoft