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Big Data Analytics is a complete process of examining large sets of data through varied tools and processes in order to discover unknown patterns, hidden correlations, meaningful trends, and other insights for making data-driven decisions in the pursuit of better results.
Today, Big Data is one of the most important discussions among business leaders and industry captains. We are today living in a digitally-driven world, due to which every enterprise is going after Big Data in order to derive valuable insights out of the huge amount of raw data. So, in this blog post, we will learn what Big Data Analytics is, why it is so important, and what its various features and advantages are.
Big Data Types
Big Data is primarily measured by the volume of the data. But along with that, Big Data also includes data that is coming in fast and at huge varieties. Primarily, there are three types of Big Data, namely:
- Structured Data
- Unstructured Data
- Semi-structured Data
Big Data can be measured in terms of terabytes and more. Sometimes, Big Data can cross over petabytes. The structured data includes all the data that can be stored in a tabular column. The unstructured data is the one that cannot be stored in a spreadsheet; and semi-structured data is something that does not conform with the model of the structured data. You can still search semi-structured data just like structured data, but it does not offer the ease with which you can do it on the structured data.
The structured data can be stored in a tabular column. Relational databases are examples of structured data. It is easy to make sense of the relational databases. Most of the modern computers are able to make sense of structured data.
Unstructured data, on the other hand, is the one which cannot be fit into tabular databases. Examples of unstructured data include audio, video, and other sorts of data which comprise such a big chunk of the Big Data today.
The semi-structured data includes both structured and unstructured data. This type of data sets include a proper structure, but still it might not be possible to sort or process that data due to some constraints. This type of data includes the XML data, JSON files, and others.