Relational database management systems (RDBMS) have been the one-stop solution to all storage needs. They support Structured Query Language (SQL) to make changes to the database. Data in an RDBMS is stored in the form of tables with rows and columns. Data in RDBMSes also tend to be more secure. With data experiencing tremendous growth today, there is a need for a reliable and effective database to store and process all of this data, and traditional databases fail to meet the optimal storage needs.
Drawbacks of RDBMSes
RDBMSes have the following drawbacks:
- They cannot accommodate huge volumes of data. To handle large volumes of data, RDBMSes have to add more storage or more processing units to scale up vertically. Horizontal scalability involves adding new servers and spreading the load across them. It is difficult to run read/write operations in parallel in an RDBMS; so, it is scaled vertically.
- Most of the data generated today are in a semi-structured or an unstructured format. Relational databases cannot store unstructured data because RDBMSes are schema-oriented and can store only structured data in the form of tables.
- Big data is generated at a very high velocity. Relational databases cannot provide high velocity, i.e., they cannot deliver the optimal speed at which big data is generated and needs to be processed.