Nosb Advantages And Disadvantages

Data is ever increasing. We need a system to represent, store and manipulate complex information, detect correlations and patterns, construct data models etc. Furthermore, being independently maintained, data can change in time or even change its base structure, making it difficult for modelling systems to accommodate these changes. Current representation and storage systems are not very flexible in dealing with structural changes and also they are not powered with the ability of performing complex data manipulations of the sort mentioned above.

In order to overcome these limitations, a new database model known as Not Only SQL (NoSQL) database emerged with a set of new features. The main objective of NoSQL is not to discard SQL, but to be used as an alternative database data model for new features [1] [2] [3]. NoSQL database increases the performance of relational databases by a set of new characteristics and advantages. In contrast to relational databases, NoSQL databases introduced an additional feature that provides flexible and horizontal scalability and taking advantage of new clusters. The rise of NoSQL provides cost-effective management of data in modern web applications. With its new features, NoSQL can be used with applications that have a large transaction, and require low-latency access to huge datasets, service availability while

Research topic was derived from the understanding of query processing in MySQL and Hadoop, the database performance issues, performance tuning and the importance of database performance. Thus, it was decided to develop a comparative analysis to observe the effectiveness of the performance of MySQL (non cluster) and Hadoop in structured and unstructured dataset (Rosalia, 2015). Furthermore, the analysis included a comparison between those two platforms in two variance of data size.

For the purpose of this paper, we are going to focus on these three type of NoSQL database BigTable, Cassandra, DynamoDB.

As Big Data grew and environments supporting Big Data become more robust, the data being stored by businesses evolved in complexity as well. All manner of nonstandard text (music, images, freeform text, videos) began being captured in the Big Data ecosystem. The changing needs of the data environment resulted in the creation of NoSQL databases. These databases (built on work done at Google and at Amazon) were optimized to store and retrieve data modeled using non-traditional non-tabular entities. And of course, Big Data cannot exist in a vacuum – it requires tools to process, analyze, and display the vast amounts of information in a manner comprehensible to mere humans. This has led to the rise of Big Data BI.

NoSQL was created to remedy the architecture of relational databases, to make the schema more dynamic and ever expanding. With the emergence of cloud computing, unstructured data such as social media posts in need of storage, and Agile development practice, The Document

NOSQL is an emerging class of non-relational database, used to handle Big Data, it stands for Not Only SQL which solve the problem of processing unstructured data, considering that this non-relational database does not use a schema, and does not relay on the table/key model used in RDBMSs (Relational DataBase Management System).

Column-based or wide column NOSQL systems: These systems segment a table by column into column families where every column family is put away in its own records. They additionally permit forming of data qualities. Chart based NOSQL systems: Data is spoken to as graphs, and related hubs can be found by navigating the edges utilizing way expressions Data with the accompanying attributes is appropriate for a NoSQL system firstly, Data volume becoming quickly secondly, Columnar development of data then, Document and tuple data Lastly, Hierarchical and graph data. Data with the accompanying qualities may be more qualified for a conventional relational database management system is On-Line Transaction Processing required atomicity, consistency, disengagement, toughness prerequisites (ACID) then Complex data relationship and Complex question prerequisites [2] Apache Cassandra are example of BigTable-style Databases Oracle Coherence, Kyoto Cabinet is case of of Key-Value Stores. mongo DB and Couch DB is example of document database and neo4j and flock dB is case of graph database. [4]. I have selected document base data modeling to compare and contras with relational data modeling.

For example, Facebook which is the most popular social networking website recently announced their adoption of a NoSQL based graph data store for efficient storage of user data. In other words, NoSQL has already made its way into the enterprise. However, just like every other widely accepted technology, NoSQL has its own set of advantages and disadvantages. It is important for an enterprise to quantify the pros and cons of a particularly new database technology against the already existing solutions based on their custom requirements. For example, legacy enterprise applications may require extensive community support from their database vendors. Moreover, traditional relational database vendors such as Oracle have already established themselves for providing excellent support. On the other hand, NoSQL has been rapidly growing since the past few years and is consistently evolving in terms of big data handling, data warehousing and lesser complexity. Hence, there is a need to study the current market of data stores based on the most popular NoSQL data stores and how well they fair against the widely accepted traditional database systems. This requires a study of the commonly used NoSQL data stores.

NoSQL databases are a significant departure from the relational model that has dominated the business world for the past few decades. Standing for “Not Only SQL,” these products are all some variation of a non-relational, key-value pair database, and they are becoming very popular with companies that use Big Data and prioritize speed or availability over consistency of data.

NoSQL databases had made for unraveling the Big Data issue by utilizing a distributed system to bring out excellent performance in data storage and retrieval at very large-scale. At this scale, pieces of the system often fail and NoSQL is created to handle these failures (Chow, 2013) (Ron, Shulman-Peleg, & Bronshtein, 2015). Various companies have espouse different sorts of non-relational databases, ordinarily alluded to as

The demands on database technology have been ever expanding since its introduction in the 1960’s. Today traffic on the internet requires that millions upon millions of records be stored and queried each second. Data must be highly available and quickly retrievable. These requirements put together have given rise to new forms of database technologies collectively called “NoSQL” or “Not Only SQL”. NoSQL eschews the strict guidelines that govern the creation and function of traditional relational databases. These guidelines are put aside in order to rise to the new demands of an increasingly interconnected world. The rigorous standards and data definitions of relational databases give way in order to provide the ability to rapidly

The modern RDBMS advancements are not capable of supporting unstructured information with ideal space necessity. The plan winds up plainly mind-boggling and is henceforth troublesome for designers. The requirement for unstructured information administration is so annoying with conventional RDBMS arrangements (Big data in financial services industry: Market trends, challenges, and prospects 2013 - 2018). Moreover, RDBMS turns out to be an exorbitant answer for creating light-footed web applications with direct information investigation necessities. NoSQL is developing as a proficient possibility in this situation, which connects the issues related with RDBMS innovation. The market development can credit to creative dispatches of NoSQL arrangements, and collective endeavors by NoSQL sellers and clients. The endeavors of organizations, to enhance their market offerings, are creating the request of NoSQL, as a back-end bolster (Big data in financial services industry: Market trends, challenges, and prospects 2013 - 2018). The emergence of agile software development is creating the demand for NoSQL (Big data in financial services industry: Market trends, challenges, and prospects 2013 - 2018). They offer users much more avenues to accept data in many different forms. NoSQL is adaptable as SQL but offers many more uses that can apply to many organizations.

NoSQL Databases are being used in the social media applications and big data processing based portals in which huge, heterogeneous and unstructured data formats are handled. NoSQL Databases are used for faster access of records from the big dataset at back-end. The AADHAAR Card implementation in India was done using NoSQL Databases as huge amount of information is associated including Text Data, Images, Thumb Impressions and Iris Detection. Any classical database system cannot handle the dataset of different types (Image, Text, Video, Audio, Video, Thumb Impressions for Pattern Recognition, Iris Sample) simultaneously.

Currently, there are two major of database management systems which are used to deal with data, the first one called Relational Database Management System (RDBMS) which is the traditional relational databases, it deals with structured data and have been popular since decades from 1970, while the second one called Not only Structure Query Language databases (NoSQL), they have been dealing with semi-structured and unstructured data; the NoSQL term was introduced for the first time in 1998 by Carlo Strozzi and Eric Evans reintroduced the term NoSQL in early 2009, and now the NoSQL types are gaining their popularity with the development of the internet and the social media. NoSQL are intending to override the cons of RDBMS, such as fixed schemas, JOIN operations and handling the scalability problems. With the appearance of Big Data,