Unstructured data has a much higher volume than structured data. This is because unstructured data comes in a variety of formats like text, images, audio and video. It requires more storage space compared to structured data which is stored in a compact manner in databases.
As per estimates, unstructured data accounts for over 80% of all enterprise data. The volume of unstructured data is increasing rapidly, driven by sources like social media, IoT devices, sensors etc.
The velocity or rate of data generation is higher for unstructured data. This is because sources like social media, emails, customer interactions generate unstructured data in real time.
On the other hand, structured data is mostly generated from well defined business processes and transactions which happen at a slower rate.
Unstructured data has a much higher variety in terms of data types and formats compared to structured data. It includes text documents, images, audio files, video files etc. This diversity of unstructured data makes it difficult to process and analyze.
Structured data on the other hand has a lower variety, existing in a well defined format like rows and columns in relational database tables.
Structured data is mostly stored in relational databases which use SQL as the query language. This makes it easy to search, organize and manage structured data.
On the other hand, unstructured data is stored in non-relational databases like NoSQL databases. This is because the schema-less and flexible nature of NoSQL databases makes it suitable for storing the diverse formats of unstructured data.
In summary, structured data has a lower Volume, Velocity and Variety compared to unstructured data. This difference is reflected in the choice of databases, with SQL databases used for structured data and NoSQL databases used for unstructured data.
A centralized database system stores data in a single location, making it easily accessible and managed by a single authority. This can provide greater security and consistency, as well as improved performance and scalability, as all data is stored in a single location. However, it can also be a single point of failure and may not be suitable for distributed systems or applications with varying data needs.