This free eLearning course facilitates the avoidance of the pitfalls commonly encountered when experienced VMware vSphere professionals cross the chasm of Tier-1 SQL Server virtualization.
- Describe how to design and implement SQL Server database on VMware.
- Describe how to design for uptime and performance.
- Discuss how to leverage VMware products and technologies.
- Discuss various SQL Server licensing scenarios.
The course consists of five modules:
- Introduction to SQL Server Database Virtualization discusses virtualization trends. This module also covers vSphere performance transparency, customer perceptions, and common objections to virtualization of Microsoft SQL Server.
- Physical Stack Fundamentals discusses Microsoft SQL Server licensing concepts. This module also covers reference architecture of SQL Server database on vSphere. In addition, the module discusses several storage, vSphere host sizing, and networking considerations.
- Virtual Machine Layer Fundamentals discusses how to configure guest Windows OS. This module also discusses various storage presentation options and compares their pros and cons. Finally, this module discusses how to optimally install SQL Server instance.
- SQL Server Database on vSphere Prototype Project discusses project management and team dynamics of the prototype project. This module also details techniques for baselining performance and discusses considerations for selection of a viable workload candidate for virtualization prototye. Finally, this module covers the process by which an organization validates the prototype’s performance.
- Beyond the Prototype Implementation discusses two disaster recovery architectures – one with SQL database mirroring and one without SQL database mirroring. This module compares single-instance SQL Server on VMware with MFC-on-vSphere. This module also discusses the logical reference architecture for MFC. This module covers vSphere’s security advantages compared to native hardware. Finally, this module reviews some prominent opportunities to apply tooling to optimize every qualitative, operational, and financial aspect of the preproduction lifecycle.