Cloud Analytics with Microsoft Azure: Transform your business with the power of analytics in Azure
Learn to extract actionable insights from your big data in real time using a range of Microsoft Azure features


• Updated with the latest features and new additions to Microsoft Azure

• Master the fundamentals of cloud analytics using Azure

• Learn to use Azure Synapse Analytics (formerly known as Azure SQL Data Warehouse) to derive real-time customer insights

Cloud Analytics with Microsoft Azure serves as a comprehensive guide for big data analysis and processing using a range of Microsoft Azure features. This book covers everything you need to build your own data warehouse and learn numerous techniques to gain useful insights by analyzing big data

The book begins by introducing you to the power of data with big data analytics, the Internet of Things (IoT), machine learning, artificial intelligence, and DataOps. You will learn about cloud-scale analytics and the services Microsoft Azure offers to empower businesses to discover insights. You will also be introduced to the new features and functionalities added to the modern data warehouse.

Finally, you will look at two real-world business use cases to demonstrate high-level solutions using Microsoft Azure. The aim of these use cases will be to illustrate how real-time data can be analyzed in Azure to derive meaningful insights and make business decisions. You will learn to build an end-to-end analytics pipeline on the cloud with machine learning and deep learning concepts.

By the end of this book, you will be proficient in analyzing large amounts of data with Azure and using it effectively to benefit your organization.


• Explore the concepts of modern data warehouses and data pipelines

• Discover unique design considerations while applying a cloud analytics solution

• Design an end-to-end analytics pipeline on the cloud

• Differentiate between structured, semi-structured, and unstructured data

• Choose a cloud-based service for your data analytics solutions

• Use Azure services to ingest, store, and analyze data of any scale

This book is designed to benefit software engineers, Azure developers, cloud consultants, and anyone who is keen to learn the process of deriving business insights from huge amounts of data using Azure.

Though not necessary, a basic understanding of data analytics concepts such as data streaming, data types, the machine learning life cycle, and Docker containers will help you get the most out of the book.

1138661963
Cloud Analytics with Microsoft Azure: Transform your business with the power of analytics in Azure
Learn to extract actionable insights from your big data in real time using a range of Microsoft Azure features


• Updated with the latest features and new additions to Microsoft Azure

• Master the fundamentals of cloud analytics using Azure

• Learn to use Azure Synapse Analytics (formerly known as Azure SQL Data Warehouse) to derive real-time customer insights

Cloud Analytics with Microsoft Azure serves as a comprehensive guide for big data analysis and processing using a range of Microsoft Azure features. This book covers everything you need to build your own data warehouse and learn numerous techniques to gain useful insights by analyzing big data

The book begins by introducing you to the power of data with big data analytics, the Internet of Things (IoT), machine learning, artificial intelligence, and DataOps. You will learn about cloud-scale analytics and the services Microsoft Azure offers to empower businesses to discover insights. You will also be introduced to the new features and functionalities added to the modern data warehouse.

Finally, you will look at two real-world business use cases to demonstrate high-level solutions using Microsoft Azure. The aim of these use cases will be to illustrate how real-time data can be analyzed in Azure to derive meaningful insights and make business decisions. You will learn to build an end-to-end analytics pipeline on the cloud with machine learning and deep learning concepts.

By the end of this book, you will be proficient in analyzing large amounts of data with Azure and using it effectively to benefit your organization.


• Explore the concepts of modern data warehouses and data pipelines

• Discover unique design considerations while applying a cloud analytics solution

• Design an end-to-end analytics pipeline on the cloud

• Differentiate between structured, semi-structured, and unstructured data

• Choose a cloud-based service for your data analytics solutions

• Use Azure services to ingest, store, and analyze data of any scale

This book is designed to benefit software engineers, Azure developers, cloud consultants, and anyone who is keen to learn the process of deriving business insights from huge amounts of data using Azure.

Though not necessary, a basic understanding of data analytics concepts such as data streaming, data types, the machine learning life cycle, and Docker containers will help you get the most out of the book.

38.99 In Stock
Cloud Analytics with Microsoft Azure: Transform your business with the power of analytics in Azure

Cloud Analytics with Microsoft Azure: Transform your business with the power of analytics in Azure

Cloud Analytics with Microsoft Azure: Transform your business with the power of analytics in Azure

Cloud Analytics with Microsoft Azure: Transform your business with the power of analytics in Azure

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$38.99 

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Overview

Learn to extract actionable insights from your big data in real time using a range of Microsoft Azure features


• Updated with the latest features and new additions to Microsoft Azure

• Master the fundamentals of cloud analytics using Azure

• Learn to use Azure Synapse Analytics (formerly known as Azure SQL Data Warehouse) to derive real-time customer insights

Cloud Analytics with Microsoft Azure serves as a comprehensive guide for big data analysis and processing using a range of Microsoft Azure features. This book covers everything you need to build your own data warehouse and learn numerous techniques to gain useful insights by analyzing big data

The book begins by introducing you to the power of data with big data analytics, the Internet of Things (IoT), machine learning, artificial intelligence, and DataOps. You will learn about cloud-scale analytics and the services Microsoft Azure offers to empower businesses to discover insights. You will also be introduced to the new features and functionalities added to the modern data warehouse.

Finally, you will look at two real-world business use cases to demonstrate high-level solutions using Microsoft Azure. The aim of these use cases will be to illustrate how real-time data can be analyzed in Azure to derive meaningful insights and make business decisions. You will learn to build an end-to-end analytics pipeline on the cloud with machine learning and deep learning concepts.

By the end of this book, you will be proficient in analyzing large amounts of data with Azure and using it effectively to benefit your organization.


• Explore the concepts of modern data warehouses and data pipelines

• Discover unique design considerations while applying a cloud analytics solution

• Design an end-to-end analytics pipeline on the cloud

• Differentiate between structured, semi-structured, and unstructured data

• Choose a cloud-based service for your data analytics solutions

• Use Azure services to ingest, store, and analyze data of any scale

This book is designed to benefit software engineers, Azure developers, cloud consultants, and anyone who is keen to learn the process of deriving business insights from huge amounts of data using Azure.

Though not necessary, a basic understanding of data analytics concepts such as data streaming, data types, the machine learning life cycle, and Docker containers will help you get the most out of the book.


Product Details

ISBN-13: 9781800200289
Publisher: Packt Publishing
Publication date: 01/28/2021
Sold by: Barnes & Noble
Format: eBook
Pages: 184
File size: 12 MB
Note: This product may take a few minutes to download.

About the Author

Has Altaiar is a software engineer at heart and a consultant by trade. Has lives in Melbourne, Australia, and is the Executive Director at vNEXT Solutions. His work focuses on data, IoT, and AI on Microsoft Azure, and two of his latest IoT projects won multiple awards. Has is also a Microsoft Azure MVP and a regular organizer and speaker at local and international conferences, including Microsoft Ignite, NDC, and ServerlessDays. Hes also a board member of the Global AI Community. You can follow him on Twitter at hasaltaiar.


Jack Lee is a senior Azure certified consultant and an Azure practice lead with a passion for software development, cloud, and DevOps innovations. He is an active Microsoft tech community contributor and has presented at various user groups and conferences, including the Global Azure Bootcamp at Microsoft Canada. Jack is an experienced mentor and judge at hackathons and is also the president of a user group that focuses on Azure, DevOps, and software development. He is the co-author of Azure for Architects and Cloud Analytics with Microsoft Azure, published by Packt Publishing. He has been recognized as a Microsoft MVP for his contributions to the tech community. You can follow Jack on Twitter at jlee_consulting.


Michael Pena is an experienced technical consultant based in Sydney, Australia. He is a Microsoft MVP and a certified professional with over 10 years of experience in data, mobile, cloud, web, and DevOps. Throughout these years, he wore various hats but considered himself a developer at heart. He is also an international speaker, having spoken at numerous events, including the Microsoft Ignite, NDC, DDD, Cross-Platform Summit, and various in-person and virtual meet-ups. Michael has interned with Microsoft and is also a Microsoft student partner alumnus. You can follow him on Twitter at mjtpena.

Table of Contents

Table of Contents
  1. Introducing analytics on Azure
  2. Introducing the Azure Synapse Analytics workspace and Synapse Studio
  3. Processing and visualizing data
  4. Business use cases
  5. Conclusion
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