Showing posts with the label traditional data warehouse

Featured Post

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

The story Hadoop data value less in cost than ETL

Traditional data warehouse That isn’t to say that Hadoop can’t be used for structured data that is readily available in a raw format; because it can.In addition, when you consider where data should be stored, you need to understand how data is stored today and what features characterize your persistence options.  Consider your experience with storing data in a traditional data warehouse. Typically, this data goes through a lot of rigor to make it into the warehouse.  Builders and consumers of warehouses have it etched in their minds that the data they are looking at in their warehouses must shine with respect to quality; subsequently, it’s cleaned up via cleansing, enrichment, matching, glossary, metadata, master data management, modeling, and other services before it’s ready for analysis.  Obviously, this can be an expensive process. Because of that expense, it’s clear that the data that lands in the warehouse is deemed not just of high value, but it has a broad purpose: it