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Implementing Microsoft Hyper-V on Data ONTAP (IMPMSHV)
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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.
- 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
- NetApp employees, channel partners, and customers
- NetApp core technical training or equivalent knowledge
- NetApp SAN core technical training or equivalent knowledge
- Basic knowledge of Microsoft virtualization
Career & Salary Insight
|Guaranteed To Run||Guaranteed To Run|
|Learning Style||Multi-Location Classroom|
|Course Duration||2 Days|
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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.