Big Data Techniques is a grouping of methods that are used to analyze huge and diverse data sets. The data can be as huge as zettabytes, and use advanced analytical technologies. It could contain structured, semistructured and unstructured data. It could come from a variety of sources and is generated by a myriad of applications.
Each day, customers generate an abundance of data each day when they send emails, use apps, post on social media, and respond to products or services. They also create information when they go into an establishment, speak with an agent from customer service or make a purchase online. Businesses collect all this data in the course of their operations and use it to improve customer loyalty as well as expand into new geographical areas or develop new products.
Data is usually delivered in different formats than it was in the past. It is no longer delivered in spreadsheets or databases, but comes from wearable devices, social media and various other technology platforms. It is typically unstructured text, images and videos and does not have a strict structure. This variety has helped to put the “big” into big data.
The second characteristic of big-data is speed. This refers to the speed at which data is generated and transferred. Every single one of these actions, such as sending text messages, responding to an Facebook, Instagram or credit card purchase or making a purchase, generate data that needs to be processed immediately. This speed is what makes large data difficult to manage.