Big Data is a term combined with complex and large datasets. A relational database cannot handle big data, and that’s why special tools and methods are used to perform operations on a more collection of data.
Big data implement companies to understand their business better and help them derive meaningful information from the unstructured and raw data collected on a regular basis. Big data also allows the companies to take some better business decisions backed by data Big Data can be defined as a collection of complex unstructured or semi-structured data sets which have the potential to deliver actionable insights. Read More Updates On Big Data Training
The four Vs. of Big Data are
We differentiate Big Data from traditional data by one or more of the four V’s: Volume, Velocity, Variety, and Veracity
1)Volume: Volume is the amount of data generated that must be understood to make data-based decisions.
A text file is a few kilobytes, a sound file is a few megabytes while a full-length movie is a few gigabytes.
Example for Big Data – Amazon
Example: Amazon handles 15 million customer click-stream user data per day to Suggested products.
The extremely large volume of data is a major characteristic of big data online training.
2) Velocity: Velocity measures how fast data is produced and modified and the speed with which it needs to be processed. An increased number of data sources both machine and human-generated drive velocity.
Youtube – Example for Big Data
Example: 72 hours of video are uploaded to YouTube every minute this is the velocity.
Extremely More velocity of data is another High characteristic of big data.
3) Variety: Variety defines data coming from new sources—both inside and outside of an enterprise
It can be structured, semi-structured or unstructured.
Structured data is typically found in tables with columns and rows of data. The intersection of the row and the column in a cell has a value and is given a “key,” which can be referred to in queries. Because there is a direct relationship between the column and the row, these databases are commonly referred to as relational databases. A retail outlet that stores their sales data (name of the person, the product sold, amount) in an Excel spreadsheet or CSV file is an example of structured data.
