Posted on Friday, October 11, 2019
Posted by : Admin
Big Data: How massive it is
This term refers to datasets that are massive in size. Data is being added in them continuously and they are further increasing in size. Here we are talking about petabytes(1024 TB) or maybe exabytes(1024 petabytes) of data containing records of people and other stuff from different sources around the world. This data is analyzed in order to figure out patterns, trends, and opportunities by using big data tools such as Hadoop. This is done because old data management techniques can't handle the complexity and size of this type of data.
There are 3 properties of big data which are commonly known as 3V's of big data, which are listed as follows;
It refers to the size of the massive datasets generated by websites, applications and portals. It is increasing exponentially day by day. For eg. Social media sites have billions of users which contribute some data everyday.
There are 2 categories of data: Structured and Unstructured. All data belongs to one of the above category. Structured data includes text data, tweets, posts, dates, etc. Unstructured data includes data which can't be expressed as basic meta-model like MRI, ECG images, audio files, handwritten text.
It refers to the frequency at which data is being generated and the requirement of processing it. As we discussed, the data created on sites like facebook is huge and it is dealt with big data tools such as AWS Kinesis.
Lately, Value(worth of data) and Veracity(quality and accuracy of data) are two other popular properties which are added to this group to comprise the 5 V's of Big data.
Why do we need big data analytics?
We need to change and improve with time. This technology is helpful in predicting trends based on given data which results in targeting more accurate audience. Big data analytics has resulted in greater transparency of customer preferences, their data is collected from different sources such as surveys, GPS enabled devices, social media, and their search patterns. For example, by using big data analysis, a firm can find out which products/services are highly in demand and by which group of audience and which products are less preferred. Hence this improves the quality of service provided by companies. It also provides better customer feedback.
Cost reduction is another benefit of it. Big data tools like Hadoop are initially expensive to set up but eventually result in saving financial resources when a large data is stored over a long period of time.