As inconceivable as it may seem today, the Apollo guidance computer took the first spacecraft to the moon with less than 80 kilobytes of memory. Since then, computing technology has grown at an exponential rate, as has data generation. In fact, the world’s technological capacity to store data has doubled roughly every three years since the 1980s. Just over 50 years ago, when Apollo 11 took off, the amount of digital data generated worldwide could have fit in an average laptop. According to Statista, 64.2 ZB of data were created or duplicated in 2020. They also forecasted that “the amount of data created digitally will more than double in the next five years compared to the amount produced before the invention of digital storage.
As software and technology become more advanced, non-digital systems become less viable. Digitally generated and collected data requires more advanced data management systems to manage it. Additionally, the exponential growth of social media platforms, smartphone technologies, and digitally connected IoT devices has helped create the current era of Big Data.
Types of Big Data: Structured and unstructured data: what are they?
- Structured data:This type of data is the easiest to organize and search. They may include financial data, machine logs, and demographic details. An Excel spreadsheet, with its layout of predefined columns and rows, is an excellent way to visualize structured data. Because of its easily categorized components, database administrators and designers can define basic search and analysis algorithms. Even when structured data exists in large volumes, it does not necessarily qualify as Big Data because structured data alone is relatively simple to manage and, therefore, does not meet the defining criteria of Big Data. Structured query language, or SQL, is a programming language that databases have historically used to manage structured data. IBM developed SQL in the 1970s to allow developers to build and operate the relational (spreadsheet-style) databases that were becoming prevalent at the time.
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Unstructured data:This category includes, among other data types, open customer comments, audio files, images, and posts from social media. It isn’t easy to obtain this kind of data in conventional row-column relational databases. This type of data is not easy to capture in standard row-column relational databases. Traditionally, companies that wanted to find, manage, or analyze large amounts of unstructured data had to use laborious manual processes. There was never any doubt about the potential value of studying and understanding such data, but the cost of doing so was often too exorbitant to make it worthwhile. Given the time it took, the results were often out of date before they were even delivered. Unstructured data is usually stored in data lakes, data warehouses, and NoSQL databases rather than spreadsheets or relational databases. - Data with some structure: Semi-structured data is, precisely as its name suggests, a combination of unstructured and structured data. Emails are an excellent example because they have organizational properties like sender, recipient, subject, and date, in addition to unstructured data in the message body. Alongside unstructured content, devices that make use of timestamps, semantic tags, or geotagging can also deliver structured data. An unidentified smartphone image, for example, may indicate that it is a selfie and the time and place it was taken. A modern database running AI technology can not only instantly identify different types of data. Still, it can also generate real-time algorithms to manage and analyze the disparate data sets involved effectively.