Название: Azure SQL Hyperscale Revealed: High-performance Scalable Solutions for Critical Data Workloads Автор: Zoran Barac, Daniel Scott-Raynsford Издательство: Apress Год: 2023 Страниц: 459 Язык: английский Формат: pdf Размер: 29.1 MB
Take a deep dive into the Azure SQL Database Hyperscale Service Tier and discover a new form of cloud architecture from Microsoft that supports massive databases. The new horizontally scalable architecture, formerly code-named Socrates, allows you to decouple compute nodes from storage layers. This radically different approach dramatically increases the scalability of the service. This book shows you how to leverage Hyperscale to provide next-level scalability, high throughput, and fast performance from large databases in your environment.
The book begins by showing how Hyperscale helps you eliminate many of the problems of traditional high-availability and disaster recovery architecture. You’ll learn how Hyperscale overcomes storage capacity limitations and issues with scale-up times and costs. With Hyperscale, your costs do not increase linearly with database size and you can manage more data than ever at a lower cost.
The book teaches you how to deploy, configure, and monitor an Azure SQL Hyperscale database in a production environment. The book also covers migrating your current workloads from traditional architecture to Azure SQL Hyperscale.
What You Will Learn: Understand the advantages of Hyperscale over traditional architecture Deploy a Hyperscale database on the Azure cloud (interactively and with code) Configure the advanced features of the Hyperscale database tier Monitor and scale database performance to suit your needs Back up and restore your Azure SQL Hyperscale databases Implement disaster recovery and failover capability Compare performance of Hyperscale vs traditional architecture Migrate existing databases to the Hyperscale service tier
Who This Book Is For: SQL architects, data engineers, and DBAs who want the most efficient and cost-effective cloud technologies to run their critical data workloads, and those seeking rapid scalability and high performance and throughput while utilizing large databases
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