Azure Well-Architected Framework

This is a continuation of a series of articles on Azure services from an operational engineering perspective with the most recent introduction of this topic with the link here. The previous article discussed the Microsoft Graph Data Connect used with Microsoft Graph. This article discusses cloud data governance and the Azure well-architected framework for data workloads.

The Cloud Adoption Framework helps to create an overall cloud adoption plan that guides programs and teams in their digital transformation. The plan methodology provides templates to create backlogs and plans to build necessary skills across the teams. It helps rationalize the data estate, prioritize the technical efforts, and identify the data workloads. Its important to adhere to a set of architectural principles which help guide development and optimization of the workloads. The Azure Well-architected framework lays down five pillars of architectural excellence which include:

-          Reliability

-          Security

-          Cost Optimization

-          Operational Excellence

-          Performance efficiency

 The elements that support these pillars are Azure well-architected review, azure advisor, documentation, patterns-support-and-service offers, reference architectures and design principles.

This guidance provides a summary of how these principles apply to the management of the data workloads.

Cost optimization is one of the primary benefits of using the right tool for the right solution. It helps to analyze the spend over time as well as the effects of scale out and scale up. The Azure Advisor can help improve reusability, on-demand scaling, reduced data duplication, among many others.

Performance is usually based on external factors and is very close to customer satisfaction. Continuous telemetry and reactiveness are essential to tuned up performance. The shared environment controls for management and monitoring create alerts, dashboards, and notifications specific to the performance of the workload. Performance considerations include storage and compute abstractions, dynamic scaling, partitioning, storage pruning, enhanced drivers, and multilayer cache.

Operational excellence comes with security and reliability. Security and data management must be built right into the system at layers for every application and workload. The data management and analytics scenario focus on establishing a foundation for security. Although workload specific solutions might be required, the foundation for security is built with the Azure landing zones and managed independently from the workload. Confidentiality and integrity of data including privilege management, data privacy and appropriate controls must be ensured. Network isolation and end-to-end encryption must be implemented. SSO, MFA, conditional access and managed service identities are involved to secure authentication. Separation of concerns between azure control plane and data plane as well as RBAC access control must be used.

The key considerations for reliability are how to detect change and how quickly the operations can be resumed. The existing environment should also include auditing, monitoring, alerting and a notification framework.

In addition to all the above, some consideration may be given to improving individual service level agreements, redundancy of workload specific architecture, and processes for monitoring and notification beyond what is provided by the cloud operations teams.