MapReduce and Design Patterns - Numerical Summarization Pattern Overview

We are going to discuss numerical summarization pattern overview

We know that we are supposed to deal with huge data in our big data

And here we are trying to get some summary out of the similar data

And to get that one, we are supposed to do some summarization design pattern implementation

So in this case, we are having multiple different types of categories

So let me discuss there is a numerical summarization pattern overview where obviously

the calculation will be done on some numerical field. So, what is numerical summarization pattern

The numerical summarization pattern is used to find the statistical values over the dataset

Statistical values means we can go for, say, mean media mode, standard deviations, we

can go for variance, we can count the number of times or the count of the key values

are appearing and so on. The intent is very simple. We should group the whole data set

on key values and then perform the numerical operations for each of the group to get the top level view of the larger data That means here we are not interested for individual data but here we are going to get

some conclusive data, some summarized data, that is the top-level view of the data

So, there are mainly two conditions to satisfy for numerical summarization patterns

So, two conditions are there. The first one is that we need to deal with the numerical data

because if I want to calculate the variance standard division average, then obviously we need to deal with the numerical data only

And the data can be grouped by some specific fields. Then obviously because if we can form groups, then only we can go to get some conclusive result

Different numerical summarization pattern. In this section we will discuss three different types of numerical summarization patterns

and they are mean max count task then inverted index task and counting with counters

So, let us discuss all these different kinds of summarization patterns, numerical

summarization patterns in our next videos. Thanks for watching this