shutterstock_314455913The calculation of moving averages can help with all sorts of “real world” problems outside of hardcore mathematics. Moving companies and businesses in all sorts of other industries can glean valuable insights with an accurate moving average in-hand. Also known as a rolling average or a sliding mean, moving averages smooth out variations in a pool of information to identify important patterns that clue business owners and other relevant parties into customer behavior, data trends and beyond. Below, we take a look at how moving averages are calculated with Hadoop and its Map-Reduce paradigm.

Hadoop for Processing Moving Averages

The top framework for processing data is known as Hadoop. Select Hadoop users claim that this framework even has the potential to process so-called “big data” on fairly basic computers such as everyday laptops. Hadoop is based on a Map-Reduce paradigm that can be implemented with what is known as “queue thread communication”. By running a specialized Java code in Hadoop, one can calculate a moving average in a selected window length or even identify exponential/weighted averages if desired.

As an example, let’s take a sequence of two thousand data points that are integers numbered between 0 and 130. The sliding mean with a sample window of eight points can be calculated by using the average of points one through nine and placing that average in location one of the data that has been “smoothed”. The average of points two through nine is calculated and placed in location two and so on until the entire “smoothed” array is completed. If a time shift is not desired, the central moving average will be computed with a smooth sequence beginning at location number five and ending at location 995.

Concept Code Shows Hadoop and Map-Reduce Work for big Data and Small Data

Moving averages are frequently used by mathematicians, business owners and statisticians to smooth out so-called “noisy” data. The code used for calculating moving averages is applicable to voluminous amounts of data as well as diminutive amounts.

Proof of concept code has been conducted to prove that Hadoop and Map-Reduce are applicable to everyone from small-sized moving companies looking to crunch data for a more thorough analysis all the way to monolithic multinational corporations that are on the prowl for a means of breaking down large amounts of data for analytical purposes.

To the surprise of many, an egregiously expensive and powerful computer is not necessary to run this code and perform these highly complex calculations. It doesn’t take a rocket scientist to understand and implement this technique. If data points arrive at different points in time, they can be transferred to map reduce programs with surprising ease.