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)

Scenarios for analytics

Being able to glean insights from your data is just as important as choosing the right type of data store. Many options exist for harvesting and reporting on that data. Online Analytics Processing (OLAP) is one method that can be used to capture and transform transactional data into a structure more suited for reporting and analytics. There are several options available for compiling and reporting on OLTP data, which we will look at now.

Transactional querying

While this may be the least efficient way to compile analytics from an OLTP data store, it is possible to directly query your OLTP database. There will be performance implications, however, as you will be querying the database while also allowing writes as a natural occurrence from application or platform usage. You may wish to address the performance issues by processing transactional writes through the database infrastructure and processing reads and aggregations through in-memory temporal tables...