Showing posts with the label hadoop architecture

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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

Top Hadoop Architecture Interview Questions

The web site defines Hadoop as "a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models." Quite simply, that's the philosophy: to provide a framework that's simple to use, can be scaled easily, and provides fault tolerance and high availability for production usage. The idea is to use existing low-cost hardware to build a powerful system that can process petabytes of data very efficiently and quickly. More : Top selected Hadoop Interview Questions Hadoop achieves this by storing the data locally on its DataNodes and processing it locally as well. All this is managed efficiently by the NameNode, which is the brain of the Hadoop system. All client applications read/write data through NameNode. Hadoop has two main components: the Hadoop Distributed File System (HDFS) and a framework for processing large amounts of data in parallel using the MapReduce paradigm HDFS