Showing posts with the label apache-storm-topology-example

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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

Apache Storm Architecture Tutorial Flowchart

There are two main reasons why Apache Storm is so popular. The number one is it can connect to many sources. The number two is scalable. The other advantage is fault-tolerant. That means, guaranteed data processing. The map-reduce jobs process data analytics in Hadoop. The topology in Storm is the real data processor. The co-ordination between Nimbus and Supervisor carried by Zookeeper Apache Storm The jobs in Hadoop are similar to the topology. The jobs run as per the schedule defined. In Storm, the topology runs forever. A topology consists of many worker processes spread across many machines.  A topology is a pre-defined design to get end product using your data. A topology comprises of 2 parts. These are Spout and bolts. The Spout is a funnel for topology Two nodes in Storm Master Node: similar to the Hadoop job tracker. It runs on a daemon called Nimbus. Worker Node: It runs on a daemon called Supervisor. The Supervisor listens to the work assigned to