Book Image

Azure Integration Guide for Business

By : Joshua Garverick, Jack Lee, Mélony Qin, Trevoir Williams
Book Image

Azure Integration Guide for Business

By: Joshua Garverick, Jack Lee, Mélony Qin, Trevoir Williams

Overview of this book

Azure Integration Guide for Business is essential for decision makers planning to transform their business with Microsoft Azure. The Microsoft Azure cloud platform can improve the availability, scalability, and cost-efficiency of any business. The guidance in this book will help decision makers gain valuable insights into proactively managing their applications and infrastructure. You'll learn to apply best practices in Azure Virtual Network and Azure Storage design, ensuring an efficient and secure cloud infrastructure. You'll also discover how to automate Azure through Infrastructure as Code (IaC) and leverage various Azure services to support OLTP applications. Next, you’ll explore how to implement Azure offerings for event-driven architectural solutions and serverless applications. Additionally, you’ll gain in-depth knowledge on how to develop an automated, secure, and scalable solutions. Core elements of the Azure ecosystem will be discussed in the final chapters of the book, such as big data solutions, cost governance, and best practices to help you optimize your business. By the end of this book, you’ll understand what a well-architected Azure solution looks like and how to lead your organization toward a tailored Azure solution that meets your business needs.
Table of Contents (15 chapters)

Designing big data solutions

Big data refers to copious amounts of incoming data being sourced from several sources. The major challenge here is that we need to aggregate these different representations of data and present them in a meaningful form so that they can be consumed by technical and non-technical persons alike.

We must employ tools and services that specialize in extracting, transforming, loading, and presenting the data for this challenge. When dealing with data from diverse sources with varying formats and velocities, it becomes crucial to establish a systematic approach for storing, integrating, filtering, and refining the data. This ensures we can efficiently work with the data and derive value from it for other operations. A clearly defined data management process is necessary to handle such scenarios effectively.

A typical data transformation process involves four steps:

  1. Ingestion: Data is acquired and brought into the big data environment. The data originates...