page paper comparing SQL and NoSQL Database for Big Data.

ISSN 2348-1196 (print) International Journal of Computer Science and Information Technology Research ISSN 2348-120X (online)

Vol. 4, Issue 2, pp: (314-318), Month: April - June 2016, Available at: www.researchpublish.com

Page | 314 Research Publish Journals

Comparative Study of SQL and NoSQL

Databases to evaluate their suitability for Big

Data Application

1 Ms. Deepika V. Shetty,

2 Ms. Sana J.Chidimar

1,2 ASM Institute Of Management & Computer Studies (IMCOST) C-4, Wagle Industrial Estate, Near Mulund Check

Naka, Thane (W), Mumbai – 400604, Department of MCA, University Of Mumbai, India

Abstract: Big data application has become an imperative for companies across a wide variety of industries. One of

the critical decisions facing companies embarking on big data projects is which database to use, and often that

decision swings between SQL and NoSQL. SQL programming language have long been the top -- and, in many

cases, only --choice of database technologies for organizations. SQL has already earned its stripes in large

organizations and big data is just one more job that powerfully built. NoSQL is SQL has the impressive track

record, the large installed base, but NoSQL is making impressive gains and has many proponents. NoSQL is

increasingly being considered a possible alternative to relational databases, especially for Big Data

applications.SQL & NOSQL databases and tries to answer which of these is better for big data application in

terms of its performance, scalability, flexibility and many more.

Keywords: Big data, SQL, NO SQL.

I. INTRODUCTION

In today„s world rapid growth of computer and internet causes an efficient storage and retrieval of data. Big data requires

exceptional technologies to efficiently process large quantities of data within tolerable elapsed times. As big data is

explode in many companies. One of critical decision facing companies on bigdata project is which database to use Sql or

Nosql. So currently most industry experts prefer to work with both as the need requires.SQL is structured query language

. SQL enables increased interaction with data. It also allows a broad set of questions to be asked against a single database

design. That‟s key since data that‟s not interactive is essentially useless, and increased interactions lead to new insight,

new questions and more meaningful future interactions. SQL is standardized, allowing users to apply their knowledge

across systems and providing support for third-party add-ons and tools.SQL is orthogonal to data representation and

storage. Some SQL systems support JSON and other structured object formats with better performance and more features

than NoSQL implementations.

When we talk about Big Data in the NoSQL space, we‟re referring to reads and writes from operational databases – that

is, the online transaction processing that people interact with and engage in on a daily basis .Operational databases are not

to be confused with analytical databases, which generally look at a large amount of data and collect insights from that

data.

While the Big Data of operational databases might not appear to be as analytical when scratching the surface, operational

databases generally host large datasets with ultra-large numbers of users that are constantly accessing the data to execute

on transactions in real time. The scale to which databases must operate to manage Big Data explains the critical nature of

NoSQL, and thus why NoSQL is key for Big Data applications. Data is becoming increasingly easier to capture and

access through third parties, including social media sites. Personal user information, geographic location data, user-

ISSN 2348-1196 (print) International Journal of Computer Science and Information Technology Research ISSN 2348-120X (online)

Vol. 4, Issue 2, pp: (314-318), Month: April - June 2016, Available at: www.researchpublish.com

Page | 315 Research Publish Journals

generated content, machine-logging data and sensor-generated data are just a few examples of the ever-expanding array

being captured. Enterprises are also relying on Big Data to drive their mission-critical applications. Across the board,

organizations are turning to NoSQL databases because they are uniquely suited for these new classes of data emerging

today.

II. SQL DATABASE

SQL databases are primarily called as Relational Databases (RDBMS). SQL is equally effective at running blazingly fast

ACID transactions. The abstraction that SQL provides from the storage and indexing of data allows uniform use across

problems and data set sizes, allowing SQL to run efficiently across clustered replicated data stores. Structured Query

Language (SQL) is a proven winner that has dominated for several decades and is currently being aggressively invested in

by big data companies and organizations such as Google, Facebook, Cloudera and Apache.

Relational databases having the variety of limitations due to constant growth of stored and analysed data, e.g. the

restrictions on scalability and storage, and efficiently losing of query as the volume of data is very large, and the storing

and managing of larger databases become challenging.

Google, Amazon, Facebook, and LinkedIn are among the first companies to discover the serious limitations of SQL

database technology for supporting big data and big user‟s requirements.

III. NoSQL DATABASE

NoSQL Database, also known as “Not Only SQL” is an alternative to SQL database which does not require any kind of

fixed table schemas unlike the SQL. NoSQL generally scales horizontally and avoids major join operations on the data.

NoSQL database can be referred to as structured storage which consists of relational database as the subset.NoSQL

Database covers a swarm of multitude databases, each having a different kind of data storage model. The most popular

types are Graph, Key-Value pairs, Columnar and Document.NoSQL is a database technology driven by Cloud

Computing, the Web, Big Data and the Big Users. NoSQL now leads the way for the popular internet companies such as

LinkedIn, Google, Amazon, and Facebook - to overcome the drawbacks of the 40 year old RDBMS.

Fig: NoSQL in Big Data Application

ISSN 2348-1196 (print) International Journal of Computer Science and Information Technology Research ISSN 2348-120X (online)

Vol. 4, Issue 2, pp: (314-318), Month: April - June 2016, Available at: www.researchpublish.com

Page | 316 Research Publish Journals

HBase for Hadoop, a popular NoSQL database is used extensively by Facebook for its messaging infrastructure.HBase is

used by Twitter for generating data, storing, logging, and monitoring data around people search. HBase is used by the

discovery engine Stumble upon for data analytics and storage. MongoDB is another NoSQL Database used by CERN, a

European Nuclear Research Organization for collecting data from the huge particle collider “Hadron Collider”. LinkedIn,

Orbitz, and Concur use the Couchbase NoSQL Database for various data processing and monitoring tasks.

Overall, with the rise in Web and mobile applications, alongside emerging trends, shifting online consumer behavior and

new data classes, the projects the industry is working on require a database technology that is capable of providing the

scalable, flexible solution to manage and access data. NoSQL technologies are the only solution available to effectively

meet these needs.

Couchbase is a NoSQL database technology provider and the company behind the couchbase project. Couchbase Server,

the company‟s flagship product, is a NoSQL document-oriented database with production deployments at Amadeus,

AOL, Cisco, LinkedIn, Orbitz, Salesforce.com, Viber and hundreds of other enterprises worldwide. Couchbase is known

for its easy and reliable scalability, consistent high performance, 24x365 availability, and flexible data model for ease of

development. Couchbase is headquartered in Silicon Valley, and is funded by Accel Partners, Ignition Partners, Mayfield

Fund and North Bridge Venture Partners.

IV. COMPARING SQL OR NoSQL BETTER FOR BIG DATA APPLICATION

1. Enables Interaction:

As SQL is a declarative query language it enables interaction.By contrast, NoSQL programming innovation MapReduce

is a procedural query technique. MapReduce requires the user to not just know what they want, but additionally requires

them to state how to produce the answer. There is technical difference with two critical reasons.

First, declarative SQL queries are much easier to build which opens up database quering to analysts, operators, managers

and others. Second, abstracting what from how allows the database engine to use internal information to select the most

efficient algorithm. Change the physical layout or indexing of the database and an optimal algorithm will still be

computed. In a procedural system, a programmer needs to revisit and reprogram the original how. This is expensive and

error-prone.

2. Speed:

SQL is relational database which requires higher degree of Normalization i.e data needs to be broken down into several

small logical tables to avoid data redundancy and duplication. Normalization helps manage data in an efficient way, but

the complexity of spanning several related tables involved with normalization hampers the performance of data

processing in relational databases using SQL.

On the other hand, in NoSQL Databases such as Couchbase, Cassandra, and MongoDB, data is stored in the form of flat

collections where this data is duplicated repeatedly and a single piece of data is hardly ever partitioned off but rather it is

stored in the form of an entity. Hence, reading or writing operations to a single entity have become easier and faster.

3. Flexibility:

Relational and NoSQL data models are very different. The relational model takes data and separates it into many

interrelated tables that contain rows and columns. These tables reference each other through foreign keys that are stored in

columns as well. When a user needs to run a query on a set of data, the desired information needs to be collected from

many tables – often hundreds in today‟s enterprise applications – and combined before it can be provided to the

application.

Similarly, when writing data, the write needs to be coordinated and performed on many tables. When data is relatively

low-volume, and when it is flowing into a database at a low velocity, a relational database is usually able to capture and

store the information. But today‟s applications are often built on the expectation that massive volumes of data can be

written (and read) at speeds near real-time. NoSQL databases have a very different model. At the core, NoSQL databases

are really “NoREL,” or non-relational, meaning they do not rely on tables and the links between tables in order to store

and organize information.

ISSN 2348-1196 (print) International Journal of Computer Science and Information Technology Research ISSN 2348-120X (online)

Vol. 4, Issue 2, pp: (314-318), Month: April - June 2016, Available at: www.researchpublish.com

Page | 317 Research Publish Journals

4. For the type of data to be stored:

SQL databases are not much good fit for hierarchical data storage. NoSQL database are comparatively better for the

hierarchical data storage as it follows the key-value pair way of storing data content which is similar to JSON data.

NoSQL database are mostly preferred for large data set (i.e. for big data). Hbase is one of the examples for this.

5. Rapid Development:

NoSQL databases tend to be less complex and considerably simpler to deploy than SQL. It is easy to change how data is

stored or the queries you‟re running in NoSQL databases. Massive changes to data can be accomplished with simple

refactoring and batch processing rather than complex migration scripts and outages. It‟s even easier to take nodes in a

cluster offline for changes and add them back into a cluster as replication features will take care of syncing up data and

propagating the new data design out to the other servers in a cluster.

6. Supports JSON:

Several years ago many SQL systems added XML document support. Now, as JSON becomes a popular data interchange

format, SQL vendors are adding JSON-type support as well. There are good arguments for structured data type support

given today‟s agile programming processes and the uptime requirements of web-exposed infrastructure. Oracle 12c,

PostgreSQL 9.2, VoltDB and others support JSON – often with performance benchmarks superior to “native” JSON

NoSQL stores.

SQL will continue to win market share and will continue to see new investment and implementation. NoSQL Databases

offering proprietary query languages or simple key-value semantics without deeper technical differentiation are in a

challenging position. Modern SQL systems match or exceed their scalability while supporting richer query semantics,

established and trained user bases, broad eco-system integration and deep enterprise adoption.

7. Scalability:

The most beneficial aspect of NoSQL databases like HBase for Hadoop, MongoDB, Couchbase and 10Gen‟s is - the ease

of scalability to handle huge volumes of data. For instance, if you operate an eCommerce website similar to Amazon and

you happen to be an overnight success - you will have tons of customers visiting your website. Under such circumstances,

if you are using a relational database, i.e., SQL, you will have to meticulously replicate and repartition the database so as

to fulfill the increasing demand of the customers.

8. For DB types:

On a high-level, we can classify SQL databases as either open-source or close-sourced from commercial vendors. NoSQL

databases can be classified on the basis of way of storing data as graph databases, key-value store databases, document

store databases, column store database and XML databases.

9. Data recovery:

When it comes to data recovery especially during natural crisis in big data application nosql database are easy to recover.

As you know NOSql is unstructured database and data is stored in document form.

10. Data security:

Compare to SQL database, NOSQL does not provide security which is one of the main problem arise in big data

application.

V. CONCLUSION

The main aim of this research paper is to evaluate which database is better for big data. Developers want a very flexible

database that easily accommodates new data types and isn‟t disrupted by content structure changes from third-party data

providers. Much of the new data is unstructured and semi-structured, so developers also need a database that is capable of

efficiently storing it. Unfortunately, the rigidly defined, schema-based approach used by relational databases makes it

impossible to quickly incorporate new types of data, and is a poor fit for unstructured and semi-structured data. NoSQL

provides a data model that maps better to these needs.

ISSN 2348-1196 (print) International Journal of Computer Science and Information Technology Research ISSN 2348-120X (online)

Vol. 4, Issue 2, pp: (314-318), Month: April - June 2016, Available at: www.researchpublish.com

Page | 318 Research Publish Journals

VI. SUGGESTION

As it has been cleared compare to sql database, nosql database is a good way for big data application but queries in NoSql

is not properly implemented and are not standardized. So queries must be properly implemented using some complier.

VII. ACKNOWLEGEMENT

We thank our college IMCOST who provided insight and expertise that greatly assisted the research .We would like to

express special thanks of gratitude to Prof. Trupti Deshmukh and Prof. Sunaina Raina who helped us in doing this

research. We would also like to express special thanks of gratitude to all teaching and non teaching staff who gave us the

golden opportunity, which also helped us in doing a lot of research and we came to know about so many new things.

We would also like to thank our parents and friends who helped us a lot in finalizing this research within the limited time

frame.

REFERENCES

[1] International Journal of Advanced Research in Computer Science and Software Engineering,„SQL and NoSQL

Databases‟ by Vatika Sharma, Meenu Dave.

[2] SQL vs NoSQL Databases Differences Explained with few Examples DB by LUKE P. ISSAC on JANUARY

14,2014

[3] http://searchdatamanagement.techtarget.com

[4] International Journal of Applied Information Systems (IJAIS), „Types of NOSQL Databases and its Comparison

with Relational Databases‟ by Ameya Nayak, Anil Poriya, Dikshay Poojary.

[5] http://www.thewindowsclub.com/difference-sql-nosql-comparision

[6] http://www.digitalocean.com/community/tutorials/und-Erstanding-sql-and-nosql-databases-and-different-database-

models

[7] International Journal of Science & Engineering Research, „Modeling and Querying Data in MongoDB‟ by Rupali

Arora, Rinkle Rani Agarwal