Many organizations use a structured database to store their data in an organized way with the formatted repository. Basically, data in a structured database has a fixed field, predefined data length, and defines what kind of data is to be stored such as numbers, date, time, address, currency, and so on. In short, the structure is already defined before data gets inserted, which gives a cleaner idea of what data can reside there. The key advantage of using a structured database is data being easily stored, queried, and analyzed.
An unstructured database is the opposite of this; it has no identifiable internal structure. It can have a massive unorganized agglomerate or various objects. Mainly, the source of structured data is machine-generated, which means information generated from the machine and without human intervention, whereas unstructured data is human-generated data. Organizations use structured databases for data such as ATM transactions, airline reservations, inventory systems, and so on. In the same way, some organizations use unstructured data such as emails, multimedia content, word processing documents, webpages, business documents, and so on.
Structured databases are traditional databases that used by many enterprises for more than 40 years. However, in the modern world, data volume is becoming bigger and bigger and a common need has taken its place--data analytics. Analytics is becoming difficult with structured databases as the volume and velocity of digital data grows faster by the day; we need to find a way to achieve such needs in an effective and efficient way. The most common database that is used as a structured database in the open source world is MySQL. You will learn how to achieve this structured database as Big Data that makes complex analysis easy. First, let's look into some insights of MySQL in the next section.
