Real Time Streaming Process is Backbone of a Business

Real Time Big Data is the Streaming Process only, as the name suggests data get transferred in a real-time scenario. This nature of Streaming Technology has made it popular in our Life. Be it a Big Enterprise owner, a household or small shop; all need this technology for their benefits and growth. As we know, nothing goes uniform always. Changes and exceptions are the nature of processes. The Streaming processes are not free from any such exceptions. We call these exceptions and changes of patterns Anomalies. Big Data Anomaly Detection is a real game changer in the business industry. Detecting anomaly in a particular pattern comes as a savior sometimes, besides tracking the regular change in pattern.

The Big Data Anomaly Detection is simple to understand.

Let’s take an example of a cafe near a bus stop. The gathering of after school kids gets increasing around 3 pm which normally never happened earlier. The metadata of the CCTV shows, it has taken pattern on a daily basis. This is a positive sign if we look at the business side. However, there are challenges to this anomaly. The food supply should be in an ample amount to fill in the demand. Also, the customers are kids so the food category should be in tune with kids’ taste. Same goes with the weather condition prediction. The anomaly detection process is quick to track the temperature or change in the weather, like rain, thunder, etc.

What kinds of Anomalies get detected?

There are three different manners to detect an anomaly in stream analytics.

First, like in temperature and weather case. In this detection, we set different ranges to collect the data. If certain data goes below or beyond the range, then there is an anomaly.

Second, is tracking the instances when certain data patterns changes to form a different pattern altogether; for example, the number of people gathering in a particular time at the cafe. Likewise, visit of a particular age group within a certain timeframe, etc.

The third and the last one, gets tracked based on the relative situation. Let’s take the example of a cafe only. In summers the sale of water and other cool beverages should increase, but when sales go down drastically in summer, then it’s a serious concern. There could be many reasons for it; like poor hygiene condition of the cafe, a higher price or competition, etc. The detection leads to resolve the issue for improving the sales.

There are different ways to Anomaly Detection.

The recent technology for Big Data Anomaly detection like Machine Learning (ML) and Artificial Intelligence (AI) are quite in practice. In the ML technology machine is fed with the past data to generate the report and then gets compared with the Real Time Big Data findings. There are Traditional statistical methods also to detect them. Both the procedures in use. Different set up need a different technology. Sometimes both the technologies come handy to get to the right information.

So the process empowers clients to make a conscious decision in the wake of real data gathered.