NoSQL Databases

inventions over time in order to keep data storage and retrieval optimized and smooth. Relational database is widely used in most of the application to store and retrieve data. They work best when they handle a limited set of data. Handling real time huge volume of data like internet was inefficient in relation database systems. To overcome this problem the "NO-SQL" or "Not Only SQL" Database came into existence [1]. As the popularity of data virtualization continues to rise, companies are increasingly relying on data storage and retrieval mechanisms like NoSQL to extract tangible value out of the voluminous

Özet- Bilgisayar ve iletişim teknolojilerinin gelişimi her geçen gün daha fazla organizasyonun süreçlerini ve iş
modellerini değiştirmektedir. Bu hızlı değişim, bireysel ve kurumsal açılardan, çok büyük verilerin üretildiği, bu
verilerin saklanmasının, işlenmesinin ve yönetilmesinin çok önemli olduğu bir gerekliliğe yol açmaktadır. Bu bilgiler
ışığında yoğun okuma yazma gibi veritabanı işlemleri yapan organizasyonlar, performans ve esneklik gibi çeşitli
ölçütler nedeniyle ilişkisel veritabanı yönetim sistemleri yerine farklı veritabanı tasarımları üzerinde durmaktadırlar. Bu
veritabanı sistemlerinden bir tanesi de NoSQL adını verdiğimiz veritabanlarıdır. NoSQL sağladığı özelliklerle, ortaya
çıkan boşluğu doldurmuş ve uygulamada farkındalık yaratmıştır. Amazon, eBay, Linkedin vb e-ticaret ve e-iş
uygulayan organizasyonlar, büyüyen verileri saklamak ve farklı hizmetlerinin doğru sunumu için NoSQL‟ e
güvenmektedir.
NoSQL veritabanları, bazı organizasyonlar tarafından kendi bünyelerinde geliştirilse bile, yaygın olarak kullanılan
NoSQL veritabanları da bulunmaktadır. Bazı organizasyonlar NoSQL veritabanlarını hibrit bir yapıda ilişkisel
veritabanları ile birlikte kullanmaktadır. NoSQL; yatay ölçeklendirebilme, hız, dağıtık sistemlere uygunluk, verimlilik
vb. özellikler açısından fark yaratsa da, birden fazla tablo üzerinde birlikte işlem yapıldığı durumlarda sorun
oluşturabilmektedir. Vurgulanan farklı yapıların özellikleri açısından, bu çalışmada, NoSQL veritabanları ile ilişkisel
veritabanları karşılaştırılmalı olarak incelenmiş ve politika önerileri ortaya konulmaya çalışılmıştır.

ABSTRACT-Computer and communication technologies are changing the processes of more organizations and their
business models every day. This fast change causes a formation in which a very large amount of data are produced and
the storage, processing and managing of these data are very important from the individual and institutional aspects. In
the light of this information, organizations engaged in intensive database operations such as reading and writing, dwell
on new structures apart from relational database because of the criteria such as performance and flexibility. In such a
process NoSQL, with the qualifications it has, has filled the gap and day by day its importance and awareness are
increasing in practice. E-commerce and e-business organizations like Amazon, E-Bay and Linkedin rely on NoSQL for
their growing data and accurate representation of their different services.
Even if NoSQL is generally developed by various organizations in-house, it is being used in a hybrid structure with
relational databases in many organizations. Although NoSQL makes a difference from the point of horizontal
scalability, speed, suitability to distributed systems, efficiency and the like, in cases where the operation is performed on
multiple tables, it may create a problem because it doesn‟t allow cascading operations. In this study, NoSQL databases
and relational databases have been examined in terms of highlighted different features and policy recommendations
have been tried to be put forward.

With the substantial growth in volume and complexity of data used in the digital world large organizations need to handle a variety of unstructured data. Traditionally relational database systems have been used to store, process and retrieve data although other types of databases such as hierarchical, network, graph have existed before 1970, yet their commercial goals were not achieved until recent years. These databases (called NoSQL or Non-relational databases) are complements to relational databases and are used by world's largest organizations such as Google, Amazon and Facebook. This research compares the performance of relational and non-relational databases namely Oracle, and MongoDB by executing complex queries on a large set of data that is available in document-based mode and is converted to Oracle tables. The results show that data retrieval is significantly faster in MongoDB however some mathematical queries such as aggregation functions(sum, count, AVG) are better suited to Oracle RDBMS.

Versatility of NewSQL databases is to achieve low latency constrains as well as to reduce cost commodity nodes. Out work emphasize on how big data is addressed through top NewSQL databases considering their features. This NewSQL databases paper conveys some of the top NewSQL databases [54] features collection considering high demand and usage. First part, around 11 NewSQL databases have been investigated for eliciting, comparing and examining their features so that they might assist to observe high hierarchy of NewSQL databases and to reveal their similarities and their differences. Our taxonomy involves four types categories in terms of how NewSQL databases handle, and process big data considering technologies are offered or supported. Advantages and disadvantages are conveyed in this survey for each of NewSQL databases. At second part, we register our findings based on several categories and aspects: first, by our first taxonomy which sees features characteristics are either functional or non-functional. A second taxonomy moved into another aspect regarding data integrity and data manipulation; we found data features classified based on supervised, semi-supervised, or unsupervised. Third taxonomy was about how diverse each single NewSQL database can deal with different types of databases. Surprisingly, Not only do NewSQL databases process regular (raw) data, but also they are stringent enough to afford diverse type of data such as historical and vertical distributed system, real-time, streaming, and timestamp databases. Thereby we release NewSQL databases are significant enough to survive and associate with other technologies to support other database types such as NoSQL, traditional, distributed system, and semi-relationship to be as our fourth taxonomy-based. We strive to visualize our results for the former categories and the latter using chart graph. Eventually, NewSQL databases motivate us to analyze its big data throughput and we could classify them into good data or bad data. We conclude this paper with couple suggestions in how to manage big data using Predictable Analytics and other techniques.

Unstructured data, which is the heart of big data, are generating in every moment due to the revolution of Internet. Relational data model is not the right tool to handle such unstructured data because it has limitation of scalability, accessibility, flexibility, agility and in addition redundancy in database. In order to resolve those flaws of relational data model, a number of non-relational data models have been invented. Among those data models, this paper focuses on document oriented data model on some cosmetic products, explores the different shortcomings of relational data model compared to non-relational data model and suggests the best data model for cosmetic product.

Therapidgrowthinthedigitalworldinformofexponentiationtoaccommodatehugeamountof structured,semi-structured,unstructuredandhybriddatareceivedfromdifferentsources.Byusing theconventionaldatamanagementtools,itisquiteimpossibletomanagethissemi-structuredand unstructured data for which a non-relational database management system such as NoSQL and NewSQLareusedtohandlesuchtypesofdata.Thesetypesofsemi-structuredandstructureddata aregenerallyconsidered'BigData.'Thisarticledescribesthebasiccharacteristics,backgroundand themodelsofNoSQLusedforbigdataapplications.Inthiswork,theauthorssurveyeddifferent NoSQL characteristics used by the researchers and try to compare the strength and weakness of differentNoSQLdatabases.

Aujourd’hui le monde a compris que toute donnée quelle qu’elle soit peut devenir une information importante. Mais comment en tirer profit afin d’augmenter notre productivité tout en limitant les ressources. C’est ce que nous avons fait en utilisant les données  afin de pouvoir réglementer et gérer les capacités ressources et aussi projeter vers les futures utilisations en utilisant des outils répondant au cas de données plus ou moins structuré.
Les outils du BI ont été utilisés notamment Pentaho workbench pour la conception des cubes et pentaho serveur auquel nous avons greffé un module Saiku analytics pour la visualisation de nos données. Les bases de données NoSQL sont une excellente alternative aux bases de données relationnelles pour le stockage et l’analyse de données volumineuses. Les principaux outils et techniques de ces solutions sont le calcul distribué basé sur MapReduce, les nouvelles techniques d’indexation, l’élasticité et le sharding. En effet ces bases de données apparaissent comme une solution aux données massives s’inscrivant aussi bien dans le contexte actuelle qu’est le Big Data néanmoins il n’est pas sans remarquer que de nouvelles solutions sont développées notamment le NewSQL qui se veut en plus d’intégrer le traitement de données les volumineuses voudrait garder le contexte relationnel.

NoSQL databases outperform the traditional RDBMS due to their faster retrieval of large volumes of data, scalability, and high performance. The need for these databases has been increasing in recent years because data collection is growing tremendously. Structured, unstructured, and semi-structured data storage is allowed in NoSQL, which is not possible in a traditional database. NoSQL needs to compensate with its security feature for its amazing functionalities of faster data access and large data storage. The main concern exists in sensitive information stored in the data. The need to protect this sensitive data is crucial for confidentiality and privacy problems. To understand the severity of preserving sensitive data, recognizing the security issues is important. These security issues, if not resolved, will cause data loss, unauthorized access, database crashes by hackers, and security breaches. This paper investigates the security issues common to the top twenty NoSQL databases of the following types: document, key-value, column, graph, objectoriented, and multi-model. The top twenty NoSQL databases studied were MongoDB,

SRP Abstract-The emergence of Internet-of-Things and Big Data causing data-driven organizations a big shift towards NoSQL data stores across all operations. NoSQL databases provide variety and dynamic schema, alternatives for storing data, and flexibility in data management. These lead organizations to migrate from a traditional relational database to NoSQL database. As a result, organizations are faced with challenges that might affect their operations and migration itself. This paper will present the effect of NoSQL database migration to the roles and responsibilities of the Database Administrators (DBAs) and Software Engineers (SEs) and their collaboration strategies. This will synthesize the different collaboration strategies that the organizations are currently practicing in operation. Likewise, this will provide a proposal of mitigation strategies based on the identified challenges that the organizations might be facing as a result of the migration. Generally, the paper is a literature review and an integration of the different challenges and strategies employed by the different organization.

With the substantial growth in volume and complexity of data used in the digital world large organizations need to handle a variety of unstructured data. Traditionally relational database systems have been used to store, process and retrieve data although other types of databases such as hierarchical, network, graph have existed before 1970, yet their commercial goals were not achieved until recent years. These databases (called NoSQL or Non-relational databases) are complements to relational databases and are used by world's largest organizations such as Google, Amazon and Facebook. This research compares the performance of relational and non-relational databases namely Oracle, and MongoDB by executing complex queries on a large set of data that is available in document-based mode and is converted to Oracle tables. The results show that data retrieval is significantly faster in MongoDB however some mathematical queries such as aggregation functions(sum, count, AVG) are better s...

With the ubiquity and pervasiveness of mobile computing, together with the increasing number of social networks, end-users have learned to live and share all kinds of information about themselves. As an example, Facebook reports that it has currently 500 million active users, 200 million of which access its services on mobile systems; moreover, users that access Facebook through mobile applications are twice as active as non-mobile users, and it is used by 200 mobile operators in 60 countries [1]. More specific mobile platforms such as Foursquare, which unlike Facebook only collects location information, reports 6.5 million users worldwide, and also has a mobile presence (both with a web application and iPhone / Android applications) [2]. Contextaware architectures intend to explore this increasing number of context information sources and provide richer, targeted services to end-users, while also taking into account arising privacy issues. While multiple context management platform...

Big data is becoming a very important concept nowadays as it can handle data in different formats and structures, velocity, and huge volume. NOSQL databases are used for handling the data with these characteristics as traditional database can't be used in managing this type of data. NoSQL database design is based on horizontal scalability with the concept of BASE which supports eventual consistence and data is considered in a soft state and basically available. Although NoSQL has a lot to offer when used in big data it is still not mature enough and faces some challenges including low join performance, concurrency control and recovery. Not only this but also it is very challenging for organizations to know which NoSQL data model to use and how does it fit with its organizational needs. This paper mainly displays the different NOSQL data models and the opportunities and challenges alongside with some techniques for handling these challenges.

The NoSQL Databases are now-a-days widely used by many big organizations to handle huge datasets and they have ability for horizontal scaling. The multi-server system has many nodes connected to servers and it uses the concept of Sharding, is a method for distributing data across multiple machines. MongoDB uses sharding to support deployments with very large data sets and high throughput operations. In this system to evaluate the performance of MongoDB, an application is developed with a case study. The Application for Student Mark Information system is developed with Python Tkinter with MongoDB. Using Anaconda as an open source distribution for Python with GUI support from Tkinter is developed for maintaining the various details about students and overall mark details for the entire college. Also the application is developed using Front end as Tkinter for designing the various screens with Graphical User Interface and the necessary collections (tables in RDBMS) are created to store the students and mark details. The data entered by the user is validated before entering in to the database. As Mongo DB is available in cloud as MongoDB Atlas, the data can be stored and retrieved from anywhere from the cloud source in a secured manner. To evaluate the query performance, the method explain() provides the necessary results. Also db.CurrentOp(), methods reports the current operations running on a mongod instance, at the server level. The performance results are added at the end.

 Abstract: Relational databases are holding the maximum amount of data underpinning the web. They show excellent record of convenience and efficiency in repository, optimized query execution, scalability, security and accuracy. Recently graph databases are seen as an good replacement for relational database. When compared to the relational data model, graph data model is more vivid, strong and data expressed in it models relationships among data properly. An important requirement is to increase the vast quantities of data stored in RDB into web. In this situation, migration from relational to graph format is very advantageous. Both databases have advantages and limitations depending on the form of queries. Thus, this paper converts relational to graph database by utilizing the schema in order to develop a dual database system through migration, which merges the capability of both relational db and graph db. The experimental results are provided to demonstrate the practicability of the method and query response time over the target database. The proposed concept is proved by implementing it on MySQL and Neo4j.

Relational databases are a technology used universally that enables storage, management and retrieval of varied data schemas. However, execution of requests can become a lengthy and inefficient process for some large databases. Moreover, storing large amounts of data requires servers with larger capacities and scalability capabilities. Relational databases have limitations to deal with scalability for large volumes of data. On the other hand, non-relational database technologies, also known as NoSQL, were developed to better meet the needs of key-value storage of large amounts of records. But there is a large amount of NoSQL candidates, and most have not been compared thoroughly yet. The purpose of this paper is to compare different NoSQL databases, to evaluate their performance according to the typical use for storing and retrieving data. We tested 10 NoSQL databases with Yahoo! Cloud Serving Benchmark using a mix of operations to better understand the capability of non-relational databases for handling different requests, and to understand how performance is affected by each database type and their internal mechanisms.

Such as relational databases, most graphs databases are OLTP databases (online transaction processing) of generic use and can be used to produce a wide range of solutions. That said, they shine particularly when the solution depends, first, on our understanding of how things are connected. This is more common than one may think. And in many cases it is not only how things are connected but often one wants to know something about the different relationships in our field - their names, qualities, weight and so on. Briefly, connectivity is the key. The graphs are the best abstraction one has to model and query the connectivity; databases graphs in turn give developers and the data specialists the ability to apply this abstraction to their specific problems. For this purpose, in this paper one used this approach to simulate the route planner application, capable of querying connected data. Merely having keys and values is not enough; no more having data partially connected through joins s emantically poor. We need both the connectivity and contextual richness to operate these solutions. The case study herein simulates a railway network railway stations connected with one another where each connection between two stations may have some properties. And one answers the question: how to find the optimized route (path) and know whether a station is reachable from one station or not and in which depth.

The relational databases have shown their limits to the exponential increase in the volume of manipulated and processed data. New NoSQL solutions have been developed to manage big data. These approaches are an interesting way to build no-relational databases that can support large amounts of data. In this work, we use conceptual data modeling (CDM), based on UML class diagrams, to create a logical structure of a NoSQL database, taking account the relationships and constraints that determine how data can be stored and accessible. The NoSQL logical data model obtained is based on the Document-Oriented Model (DOM). to eliminate joins, a total and structured nesting is done on the collections of the document-oriented database. Rules of passage from the CDM to the Logical Oriented-Document Model (LODM) are also proposed in this paper to transform the different types of associations between class. An application example of this NoSQL BDD design method is realised to the case of an organization working in the e-commerce business sector.

NoSQL database management solutions are a departure from standard relational database management systems (RDBMS). Non-tabular NoSQL databases store data in a distinct way when compared to relational database tables. The data model of NoSQL databases is used to classify them. The most popular types are document, keyvalue, wide-column, and graph. They feature customizable schemas and can easily handle large amounts of data as well as significant user loads. They have adaptable schemas and can handle big amounts of data and high user loads with ease. Furthermore, their data models' flexibility and ease of use, in comparison to the relational model, can faster the development, especially in the field of cloud computing. This paper discusses the benefits of NoSQL databases, such as the ability to handle large volumes of data at high speeds with a scale-out architecture, the ability to store unstructured, semi-structured, or structured data, and the ability to easily update schemas and fields, making them developer friendly. Furthermore, this paper discusses the downsides, such as the lack of reliability features found in Relational Databases.

NoSQL is a free and open-source, scattered, extensive column store database management system intended to handle large amounts of data across many product servers, providing high obtainability and accessibility with no single point of failure. It is the easiest truly big-data database that can scale and replicate data globally in a master-less configuration. A NoSQL database delivers a mechanism for storage and recovery of data that is demonstrated in means other than the tabular relations used in relational databases. NoSQL databases usually understood by engineers as 'not only SQL databases' neither 'no SQL', it is an alternative to the most widely used relational databases. As the given name proposed, it is a substitute for SQL that uses in such a way that the SQL is co-existed. A relational database management system (RDBMS) is a database management system based on the relational model of data and it is a completely structured way of storing data. But NoSQL is an unstructured way of storing data. Is NoSQL an RDBMS? This is an article we will discuss various features of NoSQL, various advantages over RDBMS and the future of NoSQL. This article also reviews the basics of NoSQL and its usages.

ARCA è l'acronimo di Archiviazione, Ricerca, Comunicazione in Archeologia. Durante i tre anni di dottorato di ricerca è stato impostato un lavoro, utilizzando strumenti completamente open, che consiste nello sviluppo di un software specifico per la pubblicazione e l'apertura dei dati archeologici online, scalabile e riutilizzabile per progetti diversi.

Security has become one of the key features of data transmission on large database. RDBMS are used for storage purposes but with applications generating enormous amount of data, RDBMS is no longer efficient because RDBMS doesn't support quick data access and computations as it do not support processing of data in distributed manner. NoSQL databases are nowadays popular in handling the unstructured data that are available as open source databases such as MongoDB, Cassandra, etc. This paper make a detailed study on the encryption techniques of NoSQL databases especially MongoDB which becomes popular in data management. Since encryption features are not applied on handling the data in MongoDB, In this paper, security for users data is provided by using additive homomorphic asymmetric cryptosystem which encrypts the users data in MongoDB(CryptMDB) and achieve strong user's data privacy protection. This also supports the database operations over the encrypted data.

The growth and enhancement of technology in the corporate society has led to data storage and confidentiality issues. The problem arises from the management of trillions of data, generated every second in corporations, precisely known as " Big Data ". Big Data needs to be stored and managed by larger companies that do not have the right storage systems, as there is not any developed yet. The aim of this paper is to find a solution to this growing problem by analyzing gaps in the literature, and to evaluate possible solutions. This study has analyzed content from top reviewed scientific publications, to gather compared and contrasted data from articles and highlight gaps. The highlighted literature will address this problems, and find solutions by contrasting BigData management approaches of NoSQL databases; BigTable, DynamoDB, and Cassandra. The findings summarized from publications are highlighted and the main features of all three databases and their applications are displayed. The system performances are analyzed based on their consistency, availability and partition intolerance. The study concluded that Google's BigTable and Amazon's DynamoDB are also critical and efficient on their own, and also found that the combination of both systems had caused the development of Cassandra. Cassandra is now the primary focus of numerous companies to develop different applications. Furthermore, all three systems are NoSQL storage systems, and BigTable, and based on one master node approach, unlike Dynamo, and Cassandra, it follows a Peer-to-Peer system. BigTable however, with some additional features from DynamoDB has helped the development of Cassandra, which is the basis of various modern applications available both open source and socially.

This book constitutes the refereed proceedings of the 10th IEEE International Conference Beyond Databases, Architectures, and Structures, BDAS 2014, held in Ustron, Poland, in May 2014. This book consists of 56 carefully revised selected papers that are assigned to 11 thematic groups: query languages, transactions and query optimization; data warehousing and big data; ontologies and semantic web; computational intelligence and data mining; collective intelligence, scheduling, and parallel processing; bioinformatics and biological data analysis; image analysis and multimedia mining; security of database systems; spatial data analysis; applications of database systems; Web and XML in database systems.

In the era of information, humanity produces huge quantities of data measured in terms of terabytes or petabytes that is yet growing exponentially with time. This situation led to the emergence of a large number of big data systems and technologies that share similar architectures but with different implementations. The common architecture is composed of Data sources, Ingestion, Visualization, Hadoop Platform management, Hadoop Storage, Hadoop Infrastructure, Security, and Monitoring Layers. In our way for a unified abstract implementation, we proposed in a previous work a meta-model for data sources and ingestion layers. We relied on our previous comparatives studies to define key concepts of storage in Big Data to propose a meta-model for storage layer. Thus, in this paper, we are going to present our meta-model for storage layer. The main goal of this universal meta-modeling is to enable Big Data distribution providers to offer standard and unified solutions for a Big Data system.

Nowadays, the World's leading Graph Database to collect a massive amount of data that generate every day on the search engine and then used these data to announce new staff to the users. The aim of this research to propose a graph-based model for a recommendation system that deals with the restaurants and the customers data loading from a CSV file that upload to cloud service on a google drive using google spreadsheet. In this paper, a recommendation system presented and described based on Neo4j techniques using a graph database. These techniques explained, organized and implemented in a sequence order by starting with Content-based filtering, Collaborative filtering and Hybrid filtering to develop a recommendation approach in an efficient way.

Here is a standard example of Thesis proposal written for Bachelor level. This work is copyrighted, must not be re-used. However, the readers are welcome to get an idea how to prepare their proposals, which might eventually help students struggling with proposal preparation.

Relational database systems have been the standard storage system over the last forty years. Recently, advancements in technologies have led to an exponential increase in data volume, velocity and variety beyond what relational databases can handle. Developers are turning to NoSQL which is a non-relational database for data storage and management. Some core features of database system such as ACID have been compromised in NOSQL databases. This work proposed a hybrid database system for the storage and management of extremely voluminous data of diverse components known as big data, such that the two models are integrated in one system to eliminate the limitations of the individual systems. The system is implemented in MongoDB which is a NoSQL database and SQL. The results obtained, revealed that having these two databases in one system can enhance storage and management of big data bridging the gap between relational and NoSQL storage approach.

As the size of data set in cloud increases rapidly, how to process large amount of data efficiently has become a critical issue. MapReduce provides a framework for large data processing and is shown to be scalable and fault-tolerant on commondity machines. However, it has higher learning curve than SQL-like language and the codes are hard to maintain and reuse. On the other hand, traditional SQL-based data processing is familiar to user but is limited in scalability. In this paper, we propose a hybrid approach to fill the gap between SQL-based and MapReduce data processing. We develop a data management system for cloud, named SQLMR. SQLMR complies SQL-like queries to a sequence of MapReduce jobs. Existing SQL-based applications are compatible seamlessly with SQLMR and users can manage Tera to PataByte scale of data with SQL-like queries instead of writing MapReduce codes. We also devise a number of optimization techniques to improve the performance of SQLMR. The experiment results demonstrate both performance and scalability advantage of SQLMR compared to MySQL and two NoSQL data processing systems, Hive and HadoopDB.

Big data analytics (BDA) in healthcare has made a positive difference in the integration of Artificial Intelligence (AI) in advancements of analytical capabilities, while lowering the costs of medical care. The aim of this study is to improve the existing healthcare eSystem by implementing a Big Data Analytics (BDA) platform and to meet the requirements of the Czech Republic National Health Service (Tender-Id. VZ0036628, No. Z2017-035520). In addition to providing analytical capabilities on Linux platforms supporting current and near-future AI with machine-learning and data-mining algorithms, there is the need for ethical considerations mandating new ways to preserve privacy, all of which are preconditioned by the growing body of regulations and expectations. The presented BDA platform, has met all requirements (N > 100), including the healthcare industry-standard Transaction Processing Performance Council (TPC-H) decision support benchmark in compliance with the European Union (EU) and the Czech Republic legislations. Currently, the presented Proof of Concept (PoC) that has been upgraded to a production environment has unified isolated parts of Czech Republic healthcare over the past seven months. The reported PoC BDA platform, artefacts, and concepts are transferrable to healthcare systems in other countries interested in developing or upgrading their own national healthcare infrastructure in a cost-effective, secure, scalable and high-performance manner.

"For more than 15 years, Oracle MySQL has been a real structure piece in web technology and its applications, enjoying large acceptance. This is oftentimes permanently reason: MySQL offers a strong database which enables firms to make system that execute comfortably in various use cases. Yet, still it's strongest supporters acknowledge that its not architected to appurtenances the new curve of monolithic knowledge application. Dashing businesses that enjoin to control use cases square evaluate delivery to forge a different set of technologies to alternate MySQL. This article examines the why and how migrating from Oracle MySQL to those new large knowledge technologies. Dr. Kishor Atkotiya | Parag Shukla""Migration from relational database like MySQL to nosql database like Cassandra is necessary and how to migrate it using spark"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-1 , December 2016, URL: http://www.ijtsrd.com/papers/ijtsrd57.pdf
Article URL: http://www.ijtsrd.com/engineering/computer-engineering/57/migration-from-relational-database-like-mysql-to-nosql-database-like-cassandra-is-necessary-and-how-to-migrate-it-using-spark/dr-kishor-atkotiya"

With the substantial growth in volume and complexity of data used in the digital world large organizations need to handle a variety of unstructured data. Traditionally relational database systems have been used to store, process and retrieve data although other types of databases such as hierarchical, network, graph have existed before 1970, yet their commercial goals were not achieved until recent years. These databases (called NoSQL or Non-relational databases) are complements to relational databases and are used by world's largest organizations such as Google, Amazon and Facebook. This research compares the performance of relational and non-relational databases namely Oracle, and MongoDB by executing complex queries on a large set of data that is available in document-based mode and is converted to Oracle tables. The results show that data retrieval is significantly faster in MongoDB however some mathematical queries such as aggregation functions(sum, count, AVG) are better s...

Such as relational databases, most graphs databases are OLTP databases (online transaction processing) of generic use and can be used to produce a wide range of solutions. That said, they shine particularly when the solution depends, first, on our understanding of how things are connected. This is more common than one may think. And in many cases it is not only how things are connected but often one wants to know something about the different relationships in our field - their names, qualities, weight and so on. Briefly, connectivity is the key. The graphs are the best abstraction one has to model and query the connectivity; databases graphs in turn give developers and the data specialists the ability to apply this abstraction to their specific problems. For this purpose, in this paper one used this approach to simulate the route planner application, capable of querying connected data. Merely having keys and values is not enough; no more having data partially connected through joins...

The web applications and websites of the enterprises are accessed by a huge number of users with the expectation of reliability and high availability. Social networking sites are generating the data exponentially large amount of data. It is a challenging task to store data efficiently. SQL and NoSQL are mostly used to store data. As RDBMS cannot handle the unstructured data and huge volume of data, so NoSQL is better choice for web applications. Graph database is one of the efficient ways to store data in NoSQL. Graph database allows us to store data in the form of relation. In Graph representation each tuple is represented by node and the relationship is represented by edge. But, to handle the exponentially growth of data into a single server might decrease the performance and increases the response time. Data partitioning is a good choice to maintain a moderate performance even the workload increases. There are many data partitioning techniques like Range, Hash and Round robin but they are not efficient for the small transactions that access a less number of tuples. NoSQL data stores provide scalability and availability by using various partitioning methods. To access the Scalability, Graph partitioning is an efficient way that can be easily represent and process that data. To balance the load data are partitioned horizontally and allocate data across the geographical available data stores. If the partitions are not formed properly result becomes expensive distributed transactions in terms of response time. So the partitioning of the tuple should be based on relation. In proposed system, Schism technique is used for partitioning the Graph. Schism is a workload aware graph partitioning technique. After partitioning the related tuples should come into a single partition. The individual node from the graph is mapped to the unique partition. The overall aim of Graph partitioning is to maintain nodes onto different distributed partition so that related data come onto the same cluster.