NoSQL Research
Whilst enterprise computing has undergone many advancements throughout its lifetime, relational databases have remained constant. In 1998, relational databases were challenged and a new concept called NoSQL was introduced. NoSQL spreads over to non-relational databases such as Accumulo, Cassandra, Druid, HBase, and Vertica. They provide the leverage of curating systems that are both easy and reliable for performance and programs.
NoSQL databases are fitting applications that require a strong and fast response unlike traditional relational databases. NoSQL databases are quick in response and easier to scale due to the horizontal scaling and data structures. The top NoSQL databases are columnar, document, graph, and in-memory key-value. The difference between the databases are primarily in the way the data is stored, accessed, and organized. Also, their functions are different in use when there are distinct cases and applications. NoSQL’s importance can be seen in its continuous availability. It can go through multiple data sets at any time or circumstance. NoSQL also has a low latency rate which means that it has a fast response time in its ability to go through the unstructured data models. Also, it provides easy scalability for both present and future requirements.
Within my course work in MIS 2502, we have heavily focused on SQL. The primary function of SQL is using a high-level set of statements or commands that allow us to communicate with the database. This gave me a higher understanding of how systems work especially when it comes to data analytics. Though, NoSQL is a very complex concept, it encompasses the same core concept of communication in SQL.
An example of NoSQL in practice is Amazon DynamoDB. Per Amazon, it is best defined as, “…a fast and flexible NoSQL database service for all applications that need consistent, single-digit millisecond latency at any scale.” The database itself is cloud based and supports key-value and document store models. Amazon claims that it is very reliable for mobile, gaming, IoT, and other applications because of its automatic scaling. Amazon implemented DynamoDB in Tinder. Tinder uses DynamoDB performance and scalability to cater the needs of their global user base. Due to this NoSQL advancement, Tinder is able to do massive data migrations to continue their product seamlessly in microseconds.