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)

Design brainstorming

The following few sections will try to better articulate the requirements and come up with a technical solution that could satisfy these requirements.

Data ingestion

The first task for any data practitioner is to look for data, collect it, clean it, validate it, and then start the exciting part of data discovery and exploration. For the current scenario, you need to define the data sources you need to pull data from. You also need to look at how you can load data from different sources to create a single dataset that can be explored and queried easily by data analysts. Some of the source systems that you need for this use case include:

  • Sales transactions: The sales transactions can not only tell what and how many products were sold at a particular store, but they can also indicate what customers bought what products. This is because Coolies already has a loyalty program where customers scan their loyalty card as part of the checkout procedure. Coolies...