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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

AWS EMR Vs. Hadoop: 5 Top Differences

With Amazon Elastic MapReduce Amazon EMR, you can analyze and process vast amounts of data. It distributes the computational work across a cluster of virtual servers ( run in the Amazon cloud). An open-source framework of Hadoop manages it. 

AWS EMR Vs. Hadoop

Amazon EMR - Elastic MapReduce, The Unique Features

  • Amazon EMR has made enhancements to Hadoop and other open-source applications to work seamlessly with AWS.
  • For instance, Hadoop clusters running on Amazon EMR use EC2 instances as virtual Linux servers for the master and slave nodes, Amazon S3 for bulk storage of input and output data, and CloudWatch to monitor cluster performance and raise alarms.
  • Also, you can move data into and out of DynamoDB using Amazon EMR and Hive. That orchestrates by Amazon EMR control software that launches and manages the Hadoop cluster. This process is called an Amazon EMR cluster.

What does Hadoop do?

Hadoop uses a distributed processing architecture called MapReduce, in which a task maps to a set of servers for processing.

  • The results of the computation performed by those servers reduce to a single output set.
  • One node, designated as the master node, controls the distribution of tasks. The following diagram shows a Hadoop cluster with the master node directing a group of slave nodes which process the data.
  • One Master node handles multiple slave nodes. All open-source projects run on the Hadoop architecture can also be run on Amazon EMR. The most popular applications, such as Hive, Pig, HBase, DistCp, and Ganglia, are already integrated with Amazon EMR.

By running Hadoop on the Amazon EMR, you will get the following benefits of the cloud:

  1. The ability to provision clusters of virtual servers within minutes.
  2. You can scale the number of virtual servers in your cluster to manage your computation needs and only pay for what you use. 
  3. Integration with other AWS services.


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