Relational Databases will persist due to ACID, ERDs, concurrency control, transaction management, and SQL capabilities. It doesn’t help that major software can easily integrate with these databases. But, the reason why so many new ways keep popping up is due to impedance resource costs on computational systems, when data is pulled and pushed from in-memory to databases. This resource cost can compound fast with big amounts of data. Industry wants and needs to use parallel computing with clusters to store, retrieve, and manipulate big amounts of data. Data could also be aggregated into units of similarities, and data consistency can be thrown out the window, in real-life applications since they can actually be divided into multiple phases (MUSE, 2015a).
Think of a bank transaction, not all transactions you do at the same time get processed at the same time, and they may show up on your mobile device (mobile database), they may not be committed until a few hours or days later. The bank will in my case withdraw my mortgage payment from my checking on the first, but apply it on the second of every month into the loan. But, for 24 hours my payment is pending.
Thanks to the aforementioned ideas have created a movement to support “Not Only SQL” databases, best known as NoSQL, which was derived from a twitter hashtag #NoSQL. NoSQL contains Aggregate databases like key-value, document, and column friendly, as well as aggregate ignorant databases like the graph (Sadalage & Fowler, 2012). These can be schemaless databases, where data can be stored without any predefined schema. NoSQL is best for application-specific databases, not to substitute all relational databases (MUSE, 2015b).
Originally meant for open-sourced, distributed, nonrelational databases like Voldemort, Dynomite, CouchDB, MongoDB, Cassandra, it expanded in its definition and what applications/platforms it can take on. CQL is from Cassandra and was written to act like SQL in most cases, but also act differently when needed (Sadalage & Fowler, 2012), hence the No in NoSQL.
According to Cassandra Planet (n.d.), NoSQL is best for large data sets (big data, complex data, and data mining):
- Graph: where data relationships are graphical and interconnected like a web (ex: Neo4j & Titan)
- Key-Value: data is stored and index by a key (ex: Cassandra, DynamoDB, Azure Table Storage, Riak, & BerkeleyDB)
- Column Store: stores tables as columns rather than rows (ex: Hbase, BigTable, & HyperTable)
- Document: can store more complex data, with each document having a key (ex: MongoDB & CouchDB).
In Relational databases, there is a resource cost, but in as industry wants to deal with big amounts of data, we can gravitate towards NoSQL. To process all that data we may need to use parallel computing with clusters to store, retrieve, and manipulate big amounts of data.
- Sadalage, P. J., Fowler, M. (2012-08-01). NoSQL Distilled: A Brief Guide to the Emerging World of Polyglot Persistence, 1st Edition. [VitalSource Bookshelf Online]. Retrieved from https://bookshelf.vitalsource.com/#/books/9781323137376/
- My Unique Student Experience (2015a). Emergence of NoSQL Databases. Retrieved from: https://class.ctuonline.edu/_layouts/MUSEViewer/Asset.aspx?MID=1819533&aid=1819537
- My Unique Student Experience (2015b). The Pros and Cons of Schema-Less. Retrieved from: https://class.ctuonline.edu/_layouts/MUSEViewer/Asset.aspx?MID=1819533&aid=1819537
- Planet Cassandra (n.d.) NoSQL Databases Defined and Explained. Retrieved from: http://www.planetcassandra.org/what-is-nosql/