Showing posts with the label hadoop-2x-vs-3x

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The Quick and Easy Way to Analyze Numpy Arrays

The quickest and easiest way to analyze NumPy arrays is by using the numpy.array() method. This method allows you to quickly and easily analyze the values contained in a numpy array. This method can also be used to find the sum, mean, standard deviation, max, min, and other useful analysis of the value contained within a numpy array. Sum You can find the sum of Numpy arrays using the np.sum() function.  For example:  import numpy as np  a = np.array([1,2,3,4,5])  b = np.array([6,7,8,9,10])  result = np.sum([a,b])  print(result)  # Output will be 55 Mean You can find the mean of a Numpy array using the np.mean() function. This function takes in an array as an argument and returns the mean of all the values in the array.  For example, the mean of a Numpy array of [1,2,3,4,5] would be  result = np.mean([1,2,3,4,5])  print(result)  #Output: 3.0 Standard Deviation To find the standard deviation of a Numpy array, you can use the NumPy std() function. This function takes in an array as a par

Hadoop 2x vs 3x top differences

In many interviews, the first question for Hadoop developers is what are the differences between Hadoop 2 and 3. You already know that Hadoop upgraded from version 1. The below list is useful to know the differences. I have given Hadoop details in the form of questions and answers so that beginners can understand. Hadoop 2.x Vs 3.x The major change in hadoop 3 is no storage overhead. So, you may be curious about how Hadoop 3 is managing storage. My plan is for you is first to go through the list of differences and check the references section, to learn more about Hadoop storage management. References Real story of storage management in Hadoop Follow me on twitter Applyanalytics