Big Data Hadoop Certification Training Course
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Before stating with the tutorial on setting up a virtual environment in Hadoop, let’s have a brief introduction on the different modes of Hadoop clusters.
Hadoop can run in any of the following 3 modes:
Let’s look at the steps for creating a virtual environment in Hadoop. A VMware for Windows or VirtualBox for Mac, can be used for this purpose. The following demonstration is done in VirtualBox :
Step 1: Provide the name ‘Lab1’ (can be anything of your choice). Select the type as ‘Linux’ and the versions as ‘Other Linux’.
Step 2: Select the amount of memory to be allocated to the virtual machine.
Step 3: Select the option ‘Create a virtual hard drive now’
Step 4: Select hard drive file types as ‘VMDK’ and click on ‘Continue’.
Step 5: For storage of physical hard drive, select ‘Dynamically allocated’ and click on ‘continue’.
Step 6: Specify file location and the size of the file and click on ‘Create’.
A new virtual machine has been created!!!
Step 7: Select ‘CentOS-6.5-x86_64’ in ‘CD/DVD Drive’.You are specifying the parameter needed for the virtual machine.
Let’s look at the steps involved in starting a virtual machine:
Step 1: Select ‘Enter’ in the confirmation box to initialize the start up.
Step 2: Select ‘Skip’ to start installation and continue clicking on ‘Ok’
Step 3: Select ‘Re-initialize all’ and continue.
Select the appropriate response to the questions popping on the screen and the installation process will begin.
From this Big Data training Course designed by a Big Data professional, you will get 100% real-time project experience in Hadoop tools, commands and concepts.
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Course Name | Date | |
---|---|---|
Big Data Hadoop Certification Training Course | Class Starts on 23rd May,2022 23rd May MON-FRI (Weekday Batch) | View Details |
Big Data Hadoop Certification Training Course | Class Starts on 28th May,2022 28th May SAT&SUN (Weekend Batch) | View Details |
Big Data Hadoop Certification Training Course | Class Starts on 20th June,2022 20th June MON-FRI (Weekday Batch) | View Details |
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