Identifying A Nosql Data Base

Dynamo is therefore described as a store that meets the needs of these classes of services with a simple key/value interface, in this paper. Each service using dynamo runs its own dynamo instances.

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

Though non-relational databases have been around since the 1960s, many companies have used relational databases to store data[2] but over the past decade with companies generating vast amounts of data, relational databases are unable to effectively manage these large data collections[1]. An ever increasing amount of companies is now, however, turning to non-relational databases known as NoSQL databases as they are more effective at handling these large amounts of data thus the reason we have seen an increase in its popularity over the past decade[2]. The term NoSQL database which stands for Not Only SQL[3] is defined as a database that

The paper “A Comparison to Approaches to Large-Scale Data Analysis” by Pavlo, compares and analyze the MapReduce framework with the parallel DBMSs, for large scale data analysis. It benchmarks the open source Hadoop, build over MapReduce, with two parallel SQL databases, Vertica and a second system form a major relational vendor (DBMS-X), to conclude that parallel databases clearly outperform Hadoop on the same hardware over 100 nodes. Averaged across 5 tasks on 100 nodes, Vertica was 2.3 faster than DBMS-X which in turn was 3.2 times faster than MapReduce. In general, the parallel SQL DBMSs were significantly faster and required less code to implement each task, but took longer to tune and load the data. Finally, the paper talk about

Inspired in part by MapReduce, Hadoop provides a Java based software framework for distributed processing of data intensive transformation and analytics. The top three commercial database suppliers Oracle, IBM, and Microsoft have all adopted Hadoop, some within a cloud infrastructure.

On Confais et al\cite{Confais} the authors evaluate through performance analysis three “off-the-shelf” object store solutions, namely Rados, Cassandra and InterPlanetary File

The rise of Big Data and its attendant complexities has spawned a whole ecosystem to support the ever growing requirements of a 24x7 world. One of the key technologies coming out of the initial stages of Big Data has been Hadoop. Conceived in response to the rapidly growing needs of Yahoo!’s search engine, Hadoop provides a mechanism to store and collect vast amounts of data across a highly distributed environment using commodity hardware.

NoSQL database, also called as not only SQL database, is using a different data storage and retrieval mechanism from the relational tables adopted by traditional relational database management system. In the sense of CAP (Consistency, availability and tolerance) theorem, NoSQL database sacrifices some consistency features to get more availabilities and partition tolerances. In most cases, NoSQL database systems are distributed and parallel, although the RDBMS is still dominating the database market, the NoSQL databases are becoming more and more popular and in a tendency of catching up, especially in the domains of SNS and major Internet companies, which requires large-scale data storage for massively-parallel data processing across a large number of commodity servers. The purpose of this report is to understand main characteristics of the NoSQL database and compare the strengths and weaknesses of NoSQL databases over the RDBMS.

Many social networking and/or big data companies like Facebook, Twitter, Yahoo, Google and Amazon are now known for using NoSQL databases. This is because NoSQL systems are non-relational and do not structure their data in tables or typically manipulate or process the data with SQL. Having less restrictions than a relational database, NoSQL has the ability to better handle huge quantities of data in a more efficient way (Moniruzzaman, “NoSQL Database…”). This paper will dig deeper in the several characteristics of NoSQL database systems that separate them from the relational ones. It will also introduce the different models that make up the system as well and a few examples that are currently being used and becoming popular today.

Relational database management system (RDBMS) have used for many decades. However, these databases are facing several challenges with the requirements of many organizations like high scalability and availability. They cannot deal with huge amount of data and requests efficiently. As a result, famous organizations such as Google and Amazon shift from RDBMS to NoSQL databases. NoSQL databases have several features that overcome issues. This paper explains features, principles, and data models of NoSQL databases. However, the main focus of this paper is to compare and evaluate two of the most popular NoSQL databases which are MongoDB and Cassandra.

Some of the challenges faced by relational databases were the mismatch that resulted when transforming graphs into tables. On the other hand, when a database was needed only for simples tasks like logging, the relational database had too much more than what was required. Web applications have many different types of attributes which does not fit easily into a relational database, which makes it a burden to handle. For example, videos, text and source code are different types of attributes from the web, which have to be stored in various tables if relational databases are used, because of its strict schema. Qualities like these, make RDBMS, a not-so-wise choice to handle blogs and other web applications. The massive data that has to be taken care of in web applications complicates data handling for famous webpages like Amazon, Google and Facebook. Factors like trillions and trillions of read and write requests which needs to be responded with minimal or no latency, leads these organizations to maintain their own hardware in clusters of thousands. The “One solution for all” is

It accomplishes unwavering quality by recreating the information over numerous hosts, and henceforth hypothetically does not require RAID stockpiling on hosts (but rather to expand I/O execution some RAID designs are still valuable). With the default replication esteem, 3, information is put away on three hubs: two on a similar rack, and one on an alternate rack. Information hubs can converse with each other to rebalance information, to move duplicates around, and to keep the replication of information high. HDFS is not completely POSIX-consistent, in light of the fact that the prerequisites for a POSIX document framework vary from the objective objectives for a Hadoop application. The exchange off of not having a completely POSIX-agreeable document framework is expanded execution for information throughput and support for non-POSIX operations, for example,

The term “location independence” means being able to read and write to a database regardless of where that input/output operation occurs and to have any write functionality propagating from that location, so that it is available to users and machines at other sites. Such functionality is very difficult to architect for relational databases. Cassandra allows both read and write capability with its peer-to-peer architecture and thus delivers true data location independence.

NoSQL databases are databases designed to run on clusters of computers/servers, built for the ever-increasing data storage needs for websites. Devised as a way of scaling databases horizontally which is a challenge with traditional relational databases. Scaling horizontally is the ability to add more computers/servers as nodes to a database. These “clusters” work well with write-heavy systems and allow increase storage and processing power limited only by the number of connections you can have on the network. Defined as No-Schema, No-SQL data structures mean they are not limited to the original data structure. Objects and fields etc can be implemented at

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.