One of the most popular modern database systems in the workplace are relational database management systems (RDBMS) based on a relational database model. Data on this database technology is stored in tables, along with Structured Query Language (SQL) used to access the database. Popular software for this type of database includes MySQL, Oracle, and Sybase. This type of database allows for thousands of simultaneous users while ensuring a high level of security. The database is dependable in retrieving and storing large amounts of data that also combines with ease of mass implementation in organizations due to effective system performance (Zafar et al., 2016).
A database technology gaining popularity is the NoSQL database technologies based on object-oriented programming. This approach seeks to create queries and “bookshelves” of elements and give access to these elements. Instead of tracking individual search parameters, NoSQL directs the user to the necessary bookshelf to search for the information. NoSQL directly seeks to simplify storage by maintaining data in a denormalized manner, or large pieces. The object-oriented method can work with complex, heterogeneous, and rapidly changing data silos by answering user queries, maintaining data quality, and ensuring solid security (Corbellini et al., 2017).
A recently developed and innovative technology is In-Chip technology combined with elastic tubes. It combines in-memory querying with the robustness of the older technology of OLAP cubes while eliminating many hardware and implementation challenges. In-Chip is the most modern iteration of in-memory technology in database analytics, utilizing columnar database for storage, allowing for fast disk reads and loading to RAM and vice versa). Only a small subset of data is stored on RAM allowing for other operations to take place, as the technology uses advanced compression and essentially analyzes data, which is not used by businesses, this can be left ‘at rest.’ There is significant optimization as the technology utilizes columnar algebra to merge between fields instead of the traditional joining tables approach (Levy, 2018).
References
Corbellini, A., Mateos, C., Zunino, A., Godoy, D., & Schiaffino, S. (2017). Persisting big-data: The NoSQL landscape. Information Systems, 63, 1–23. Web.
Levi, E. (2018). Quick guide to database technologies. Web.
Zafar, R., Yafi, E., Zuhairi, M. F., & Dao, H. (2016). Big data: The NoSQL and RDBMS review. 2016 International Conference on Information and Communication Technology (ICICTM). Web.