Best practices for Azure readiness

Cloud readiness requires equipping staff with the technical skills needed to start a cloud adoption effort and prepare your migration target environment for the assets and workloads you'll move to the cloud. Read these best practices and additional guidance to help your team prepare your Azure environment.

Azure fundamentals

Organize and deploy your assets in the Azure environment.

Networking

Prepare your cloud networking infrastructure to support your workloads.

  • Networking decisions. Choose the networking services, tools, and architectures that will support your organization's workload, governance, and connectivity requirements.
  • Virtual network planning. Plan virtual networks based on your isolation, connectivity, and location requirements.
  • Best practices for network security. Learn best practices for addressing common network security issues using built-in Azure capabilities.
  • Perimeter networks. Enable secure connectivity between your cloud networks and your on-premises or physical datacenter networks, along with any connectivity to and from the internet.
  • Hub and spoke network topology. Efficiently manage common communication or security requirements for complicated workloads and address potential Azure subscription limitations.

Identity and access control

Design your identity and access control infrastructure to improve the security and management efficiency of your workloads.

Storage

Databases

  • Choose the correct SQL Server option in Azure. Choose the PaaS or IaaS solution that best supports your SQL Server workloads.
  • Database security best practices. Learn best practices for database security on the Azure platform.
  • Choose the right data store. Select the right data store to meet your requirements. Hundreds of implementation choices are available among SQL and NoSQL databases. Data stores are often categorized by how they structure data and the types of operations they support. This article describes several common storage models.

AI + Machine Learning

Cost management