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)

Azure Machine Learning and Azure Synapse Analytics

Azure Machine Learning and Azure Synapse Analytics can work hand in hand as they solve different problems. You use Azure Synapse Analytics for your modern data warehouse to make a unified data pipeline for your disparate data sources and eventually model and serve that data for a client consumer. Azure Machine Learning on the other hand, is used to create a machine learning model that can eventually be used for your applications to create meaningful inferences (assumptions).

A practical example of using Azure Machine Learning and Azure Synapse Analytics together could be in a physical retail store. Azure Synapse Analytics can aggregate multiple data sources such as beacon and CCTV data (unstructured), NoSQL databases, and SQL databases, then query them all to serve meaningful reports for Power BI such as "number of goods sold versus audience traffic." Using Azure Machine Learning on the other hand, you can run through...