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Question: Discuss about the Scalable SQL and NoSQL Data Stores. Answer: Introduction Over the past decade, technology has evolved greatly. This has resulted in a lot of data being generated from different platforms. The requirements for data storage and accessibility have also evolved due to increase in data types creating a problem for the conventional relational databases which is mostly used for structured data. The conventional relational databases have a strict schema and once defined it is impossible to alter it without taking the database offline. For Industries that generated a large volume of data varying in types and formats, the NoSQL database offers a flexible, and scalable alternative. NoSQL Databases NoSQL stands for not only SQL or not relational(Cattell 2011). This type of databases are normally open source and tend have a flexible data model compared to the relational databases. The NoSQL databases are distinguished by their BASE (Basically Available, Soft-state, Eventually consistent) properties which do not conform to the CAP (consistency, Availability, Partition-tolerance) theorem which requires data systems to have at least two of this properties according to Brewer (2010) as cited by Birgen (2014). NoSQL databases favour availability over consistency allowing them to accommodate a lot of read and write commands at the same time. Horizontal scalability and performance NoSQL databases allow distribution of data over different servers that do not share disk space or RAM; once a server is added, the database automatically spreads the data into the new server resulting to load distribution increases the performance of the database (Cattell 2011). This is cheaper than vertical scaling supported by SQL databases as it basic servers involves working together as clusters as opposed to a single very specialized server used by relational databases(Mongodb n.d.). Unlike in relational databases-SQL databases where the schema has to be predefined, NOSQL databases has the capacity to allow the addition of new data types and the schema evolves as new data types are added in. Limitations NoSQL databases are becoming more popular in this digital age as they are very useful in storing big data, however, there are a few areas where they may not be the best choice for everyone. They are open source and therefore lack dedicated support as compared to the relational databases who have readily available experts to help in case there is a problem. They are also not very useful for industries that require manipulation and querying of data as they mostly require manual querying of the database due to lack of a structured language (Leavitt 2010). Key-Value databases They are usually schema-less and use a simple Key and Value relationship where the key acts as a unique index while the value is the data itself (Gurevich 2015). An example of this database is e-bay where one searches for items using a keyword and the database provide the information relating to the keyword. A key value database is a hash table that is used primarily when access is made through primary key. Regarded as the simplest NoSQL database, key value database is the simplest to use. A client can put the value for the key, get its value, or delete a key. The value is just stored. The database is unaware of what lays inside. The application understands what is stored inside the data store. Riak is a key value database that helps to store keys in buckets. It is a way to segment keys. Information related to shopping cart, user preferences, and session data can be stored in the same bucket having a single value and key (Sebastian et al., 2013). Graph databases Graph databases utilize graph structures to store and restore data. The graph is the key concept of the system and relates directly to the data items. Graph databases are relatively older ones in the NoSQL database. Birgen (2014) stated that they employ nodes, edges, and properties to represent and store data. They create relationships between the different nodes and hence are best used to stored data highly interconnected data (Hazim 2014). An example that uses this type of database is facebook where it is used to create relationships between friends and even friends of friends. Neo4J can be taken as a graph database and can be used to solve problems related to application. Graphs like FlockDB and Infinite Graph provide a distributed storage for the nodes. The last transaction IDs are kept track by using Apache ZooKeeper. Java objects are stored as edges and nodes in Infinite Graph. Such databases also use the properties of location and distance so that the application used by the client can give recommendations of entertainment options or good restaurants in nearby places (Miller, 2013). Conclusion This evolution of data to big data which is not easily stored by the common relational databases has created a gap that these databases have filled. They may not be useful for everybody and are by no means on the path to replace the relational databases but are very useful for industries which do not require the consistency offered by relational databases and favour availability more. References Birgen, Cansu. Advanced Process Stimulation: SQL vs NoSQL . Technologynorwegian University Of Science And Technologynorwegian University Of Science And Technologynorwegian University Of Science And Technology Norwegian University Of Sciencnorwegian University Of Science And Technology Cattell, Rick. Scalable SQL and NoSQL data stores. SIGMOD Rec. vol. 39, no. 4, 2011: 12-27. Gurevich, Yuri. Comparative Survey of NoSQL/ NewSQL DB Systems. Department of Mathematics and Computer Science, The Open University of Israel, The Open University of Israel, 2015. Hazim, Dhulfiqar. Document - Oriented NoSQL Databases Performance analysis of Mongodb and MySQL using PHP. THAPAR UNIVERSITY, 2014. Leavitt, Neal. Will NoSQL Databases Live Up to Their Promise? Computer (IEEE Computer Society) Volume 43, no. Issue 2 (02 2010): 12-14. Mason, Robert T. NoSQL Databases and Data Modeling Techniques for a Document-oriented NoSQL Database. Informing Science IT Education Conference (InSITE). 2015. 259-268. Miller, J. J. (2013, March). Graph database applications and concepts with Neo4j. In Proceedings of the Southern Association for Information Systems Conference, Atlanta, GA, USA (Vol. 2324). Mongodb. Mongodb. https://www.mongodb.com/security (accessed 11 02, 2016). Sebastian, Abraham, Swaroop Jagadish, S. U. N. Yun, Robert M. Schulman, and Shirshanka Das. "Transactional key-value database with searchable indexes." U.S. Patent Application 13/935,130, filed July 3, 2013. Sharma, Sugam. An Extended Classification and Comparison of NoSQL Big Data Models. Iowa State University, 2015.