With the quantity of data in the world keeping on increasing every day, the term “Big Data” is growing important. The term refers to a collection of large and complex data sets that cannot be handled using traditional data processing applications. Capture, storage, sharing, analysis and visualization of big data are challenging tasks.
What Are The Characteristics Of Big Data?
The key characteristics of Big Data are:
By volume, we mean the amount of data that keeps on growing each day that too at a surprising pace. You can just look at the massive size of social media data generated by humans and machines.
The pace at which data is generated from different sources is referred to as velocity. The data flow tends to be massive and continuous. Unless you are able to handle the velocity, making decisions based on real time data will become difficult.
Many sources contribute to Big Data and thus the data types generated by each may be different. The generated data could be unstructured, structured or semi-structured. A lot of data is being generated every day in the form of images, videos, audios, sensor data, and more. The wide variety of unstructured data is difficult to capture, store, mine and analyze.
Veracity refers to data that is inconsistent or incomplete. Data that you get may sometimes be messy or difficult to trust. Data lacks quality and accuracy primarily because of its large volume.
A survey found that about 27% of the participants were unsure of the accuracy of the data they received. The uncertainty of data makes 1 in 3 business leaders not trust the information they use in decision-making. The US economy loses around $3.1 trillion a year because of poor data quality.
The benefit offered by Big Data to organizations that analyze it is regarded as its value. Unless and until you can turn Big Data into value, it is merely a large collection of useless information. Working on Big Data should increase the profits of organizations and make the time they spend on analyzing the data worth it.
Types Of Big Data
Big Data can be grouped into three categories:
Structured: Data that comes in a fixed format, thereby making processing an easy task, is referred to as structured data. A relational database management system(RDBMS) is an example.
Semi-structured: This type of data does not have a formal structure as that of a data model but has some organizational properties like tags and similar markers in order to separate semantic elements and make it easier to analyze. XML files and JSON documents are examples.
Unstructured: Data that has an unknown form and cannot be stored in RDBMS is called unstructured data. Multimedia contents like audios, images and videos are common examples of unstructured data.
Hadoop is the programming framework commonly used to deal with all Big Data challenges.