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)

Solution architecture

Now that the BI team has refined the requirements and a cloud platform has been chosen, it is time to come up with a secure and scalable design. The NIA business intelligence team went with the following solution architecture:

Solution architecture for NIA

Figure 4.6: Solution architecture for NIA

The design in Figure 4.6 shows the solution architecture and the data flow between the individual components. Here's an explanation for each of the workflow segments, as marked (numbered) in the diagram:

  1. Structured data such as Airlines Data, Custom Data, and Baggage Data is ingested using Azure Data Factory. This includes other data sources, such as data from the parking systems and weather data. ADF provides the ability for NIA to configure an integration runtime that can be used as a gateway to connect to NIA's on-premises data sources from within Azure.
  2. All unstructured data, including IoT Sensors data, Traffic Video streaming, and Social Media...