A NoSQL Database, which is also popular as ‘Not Only SQL’, offers a data retrieval and storage mechanism. It is not used for the data which is modelled in the tabular relations used in relational database. The NoSQL approach carries a number of plus points which includes its simple design, final control over availability and horizontal scaling. The NoSQL data structures like its values, graphs and documents, are totally different from the structures of relational database. A range of different database technologies, which have been developed to cater the needs of modern application building, are encompassed by the NoSQL.
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There is a huge difference between NoSQL and Relational data models. In the relational data models, data is divided into a range of inter-related tables, where foreign keys are used to reference the tables with each other. These keys are also stored in columns. The process of looking up data requires information to be gathered from many tables and combining them before delivering it to the application. In the same way, for writing data, the write must be collected and performed on various tables.
On the contrary, NoSQL databases follow a different model. For instance, a NoSQL oriented database document retrieves the data that needs to be stored and combines it with the documents in the JSON format. The application considers every JSON document as an object to be used. A JSON document may collate all the stored data pertaining to a row comprising 20 tables of a relational database to combine them to a single object or document.
Despite having a chance for data duplication, the NoSQL model is immensely used for web based applications as the cost of storage is economical, the resulting data model is highly flexible and efficient in dispersing the generated documents and renders high improvement in read and write performance.
The highly flexible NoSQL databases are effectively designed to resolve the challenge of scaling databases using a cluster of standard, virtual or physical servers to help in storage of data while supporting database operations.
For scaling purposes, more servers are amalgamated within the cluster and then the data and database operations are distributed accordingly across the enlarged cluster. To recover data and database operations from the downtimes that occur due to failure of commodity servers, the NoSQL databases are developed to be fault tolerant and highly resilient.
Scaling of databases require simpler and linear approach, which the NoSQL databases readily offer. If thousands of users start using an application, then simply adding another server to the cluster can serve the purpose through effective scaling. The need for modifying the application to accommodate the rising number of users is bypassed by NoSQL databases.
Moreover, there are various techniques relating to the implementation of NoSQL databases. But there are many peculiar features that every NoSQL database holds in terms of performance and scaling –
Without requiring any activity from the applications, the NoSQL databases are capable of distributing the data to the servers automatically. Also, without causing downtime or loss of data, addition or removal of servers can be made, as data will then get adjusted to the new tally of servers. To assure greater availability and support data recovery in case of server failure, most NoSQL databases support data replication and storage of several copies of data across the cluster or data centre. An efficiently administered NoSQL database never needs to be put on offline mode for any purpose as it is fully capable of supporting seamless operation of diverse applications on 24x7x365 basis.
Performing complex data queries become difficult when a relational database becomes subject to ‘sharding’. To cater this issue, the NoSQL databases host full query expressive power that works optimally even when data gets distributed across multiple servers.
The technologies behind NoSQL databases are powered to transparently cache data in the system memory to cause a significant decrease in latency and improve sustained data throughput.
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