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Implementing Microsoft Hyper-V on Data ONTAP (IMPMSHV)

In this course, you learn how to deploy the Microsoft® Windows® Server 2012 virtual infrastructure on Data ONTAP storage.

Multi-Location Classroom

Learning Style

NetApp

Provider

Intermediate

Difficulty

2 Days

Course Duration

24 Units

NATU

Course Info

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In this course, you learn how to deploy the Microsoft® Windows® Server 2012 virtual infrastructure on Data ONTAP storage.

Course Information

About this course:

Learn how to implement Microsoft Hyper-V virtualization solutions on clustered Data ONTAP. In this course, you learn how to deploy the Microsoft Windows Server 2012 R2 virtual infrastructure on Data ONTAP storage.

The average salary for a NetApp storage administrator is $122,000 per year.

Course Objectives:

  • Describe Microsoft Windows Server 2012 R2 Hyper-V virtualization solution
  • Articulate the NetApp value proposition when integrating the Data ONTAP operating system with virtualization solutions
  • Describe the main concepts, terminology and operation of SnapManager for Hyper-V v2.1
  • Demonstrate the configuration of SnapManager for Hyper-V v2.1 hosts, datasets, policies, event notifications, and reporting
  • Identify capabilities, functions, and integration of the Microsoft and NetApp management, backup, and migration tools in a public, private, and hybrid cloud environment

Audience:

  • NetApp employees, channel partners, and customers

Prerequisites:

  • NetApp core technical training or equivalent knowledge
  • NetApp SAN core technical training or equivalent knowledge
  • Basic knowledge of Microsoft virtualization

Outline

More Information

More Information
Brand NetApp
Subjects IT Ops & Management
Guaranteed To Run Guaranteed To Run
Lab Access Yes
Technology NetApp
Learning Style Multi-Location Classroom
Learning Type Course
Difficulty Intermediate
Course Duration 2 Days
Language English
NATU 24 Units

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Course Expert:

Author

Tom Robertson
(Data Science Enthusiast)

Tom is an innovator first, and then a Data Scientist & Software Architect. He has integrated expertise in business, product, technology and management. Tom has been involved in creating category defining new products in AI and big data for different industries, which generated more than hundred million revenue cumulatively, and served more than 10 million users.
As a Data Scientist and Software Architect Tom has extensive experience in data science, engineering, architecture and software development. To date Tom has accumulated over a decade of experience in R, Python & Linux Shell programming.

Tom has expertise on Python, SQL, and Spark. He has worked on several libraries including but not limited to Scikit-learn, Pandas, NumPy, Matplotlib, Seaborn, SciPy, NLTK, Keras, and Tensorflow.

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