Big Data is nothing but a collection of large and complex datasets, because of which it is very difficult to process this large amount of data using usual applications.
So, the traditional systems can not process this larger size data in one go. But, it's not that easy to classify the data which is hard to operate. To know in-depth about big data applications you must learn from Big Data course. This article is about brief knowledge of Big Data Hadoop.
Big Data Categorisation:
1. Volume: Volume refers to the tremendously large data. In the image below, you can see that the data volume is rising exponentially. In 2016, the data was only 8 ZB and by 2020, the data has increased to 40 ZB.
2. Variety: The above mentioned huge increase in data is because of different data sources with different variations in data.
The data is categorized as follows:
- Structured Data
- Semi-structured Data
- Unstructured Data
- Quasi-structured Data
3. Velocity: Data accumulation is responsible for determining the data category into big data or normal data.
Initially, mainframes were used and few people used computers. Then the client/server model came and then the web applications came into the market.
4. Value: Now, it's about the extraction of data. The fourth V deals with a mechanism that brings the meaning out of data. First, do the data mining i.e., a process to turn raw data into useful data. Next is an analysis to know the data that have cleaned or retrieved is proper. Then, make sure that the analysis you have done is beneficial for your business or not, which was not possible earlier.
5. Veracity: To overcome the consequences of losing data and again doing the data mining process the last V came into scene i.e., Veracity. Veracity is the trustworthiness and quality of data. It is mandatory to maintain the veracity of the data.
Now you have a brief idea about Big data and how does the big data work.