Book Image

Cloud Analytics with Microsoft Azure - Second Edition

By : Has Altaiar, Jack Lee, Michael Peña
Book Image

Cloud Analytics with Microsoft Azure - Second Edition

By: Has Altaiar, Jack Lee, Michael Peña

Overview of this book

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.
Table of Contents (7 chapters)

Insights and actions

Using Microsoft Azure, the Coolies data team was able to design, build, and deploy the solution quickly and easily. Within two weeks, the team found a number of key insights that can help Coolies increase its profit margins. Three of these insights are listed in here:

Reducing waste by 18%

Description: With initial modeling, the Coolies data team was able to reduce waste by 18%. Currently, the organization loses close to $46M per year because of overstocking products with short shelf lives. This includes products such as bread and milk. The team combined historical sales data with other sources, such as weather data and school calendars, which allowed the team to predict the demand for these products with higher accuracy, leading to a significant reduction in waste.

Estimated business value: $8.28 million/year

Key data sources: Sales transactions (online and physical store), store data (store locations and stock over time), weather data, suburb profile...