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Real-time database has two meanings. The most common use of the term refers to a database system which uses streaming technologies to handle workloads whose state is constantly changing. This differs from traditional databases containing persistent data, mostly unaffected by time. When referring to streaming technologies, real-time processing means that a transaction is processed fast enough for the result to come back and be acted on right away. Such real-time databases are useful for assisting social media platforms in the removal of fake news, in-store surveillance cameras identifying potential shoplifters by their behavior/movements, etc. The second meaning of the term “real-time database” adheres to a stricter definition of real-time consistent with []. Hard real-time database systems work with a real-time operating system to ensure the temporal validity of data through the enforcement of database transaction deadlines and include a mechanism (such as transaction scheduling policies) to maximize the number of successfully committed transactions and minimize the number of rolled-back transactions. While the performance metric for most database systems is throughput or transactions-per-second, the performance metric of a hard real-time database system is the ratio of committed-to-aborted transactions. This ratio indicates how effective the transaction scheduling policy is, with the ultimate goal of meeting deadlines 100% of the time. Hard real-time databases, through enforcement of deadlines, may not allow transactions to be late (overrun the deadline). Real-time databases are traditional databases that use an extension to give the additional power to yield reliable responses. They use timing constraints that represent a certain range of values for which the data are valid. This range is called temporal validity. A conventional database cannot work under these circumstances because the inconsistencies between the real world objects and the data that represents them are too severe for simple modifications.