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What Are Big Data Techniques?

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Big Data Techniques are a method employed to analyze large, diverse data sets. They employ advanced analytical technologies and the data could be anywhere from terabytes up to zettabytes in size. It can contain structured, semistructured and unstructured data. It can come from multiple sources and is produced by a variety of applications.

Customers generate a large amount of data each day when they send emails, use apps, engage on social media and react to products or services. They also generate data when they walk into the store, talk to an agent for customer service or make an online purchase. Businesses collect this data in the course of their day-to-day operations and use it for improving customer loyalty, expanding into new geographical areas, or creating new products.

Data is usually delivered in different formats than it was in the past. It’s no longer presented in spreadsheets or databases however, it is available through wearable devices, social media, and a variety of other technology platforms. It is usually unstructured text, images and videos and is not a rigid structure. This diversity has helped put the «big» into big data.

The second characteristic of big-data is speed. This refers to the speed at which data is created and transferred. When you send a text message, respond to an Facebook or Instagram post, or make a purchase with a credit card, all these actions create data that must be processed immediately. This speed is what makes massive data difficult to handle.

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