Two pillars of big data analysis

1. Structured Data

Structured data refers to data that enters into a relational database (row and column oriented database structures), exists in predefined fixed fields, and is findable via search operations or algorithms. Structured data is quite simple to enter, save, find and analyze; however, it must be well-defined regarding field name and character type (e.g. alpha, numeric, date, currency, etc.). Thus, structured data is often restricted in usage because of its inflexibility. Some examples of structured data are financial details, call detail records, web server logs and human input data. Analysts and programmers working on this kind of data use structured query language (SQL) technology for relational databases (RDBMS).

2. Unstructured Data

Unstructured data does not fit into a spreadsheet or data store. However, it may have its internal structure. While unstructured data seems organized in nature, it is also treasured and increasingly available in the form of complex data formats, such as emails, text files, web pages, digital images, multimedia content, navigation details and social media posts. In fact, the majority of business interactions seem unorganized in nature. There are several ways to start assembling a database of unstructured data and processing it. Many companies have migrated to object-oriented databases like MongoDB which implement NoSQL technology for storing unstructured data. Some companies are also involved in open source big data analysis techniques, like Hadoop.

For big data analytics, analysts need to integrate structured data with unstructured data, for example, mapping customer and sales automation data to social media posts or mapping client address and audio files. No matter what the complexity and variance of structured and unstructured data are, analysts should use appropriate preparation, analysis, and visualization methods to leverage all the available data for better decision-making.

Best solution for big data analysis