A Qualitative Comparison of NoSQL Data Stores

Kamal, Sarah H.; Elazhary, Hanan; Hassanein, Ehab E.

doi:10.14569/ijacsa.2019.0100244

Cited by 3 publications

(8 citation statements)

References 14 publications

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“…Key-value datastores use keys to handle data, which is similar to the use of the object-oriented structures such as maps, collections, or vectors. The basic structure of those databases gives them a high performance in term of time, especially for read and write queries [21]. Among the benefits of such datastores; the low time response and the ease of scalability.…”

Section: Nosqlmentioning

confidence: 99%

“…In addition to Apache HBase, Cassandra are widely used in social networks' applications such as the case of Facebook. The strength of Cassandra is related to its implantation of both DynamoDB key-value store and BigTable [23] column-oriented store which adds a fastness data access and high storage capacity [3,21].…”

Section: Nosqlmentioning

confidence: 99%

“…Now for the conversion of the vertices, each vertex is converted separately as follows (21). The number of nodes n is ignored by the conversion function as it can be easily calculated (22 and 23).…”

Section: Conversion To Mongodb Syntaxmentioning

confidence: 99%

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A New Algorithm for Storing and Migrating Data Modelled by Graphs

Mouden

Jakimi

2020

Int. J. Onl. Eng.

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<span>NoSQL databases have moved from theoretical solutions to exceed relational databases limits to a practical and indisputable application for storing and manipulation big data. In term of variety, NoSQL databases store heterogeneous data without being obliged to respect a predefined schema such as the case of relational and object-relational databases. NoSQL solutions surpass the traditional databases in storage capacity; we consider MongoDB for example, which is a document-oriented database capable of storing unlimited number of documents with a maximal size of 32TB depending on the machine that runs the database and also the operating system. Also, in term of velocity, many researches compared the execution time of different transactions and proved that NoSQL databases are the perfect solution for real-time applications. This paper presents an algorithm to store data modeled by graphs as NoSQL documents, the purpose of this study is to exploit the high amount of data stored in SQL databases and to make such data usable by recent clustering algorithms and other data science tools. This study links relational data to document datastores by defining an effective algorithm for reading relational data, modelling those data as graphs and storing those data as NoSQL documents.</span>

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Section: Nosqlmentioning

confidence: 99%

Section: Nosqlmentioning

confidence: 99%

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A New Algorithm for Storing and Migrating Data Modelled by Graphs

Mouden

Jakimi

2020

Int. J. Onl. Eng.

View full text Add to dashboard Cite

show abstract

“…They also fit for storing semi-structured data that would require using extensive Null values for data completion purpose [13]. Examples of Document-Based NoSQL DBMSs include -but are not limited to -CouchDB (JSON), Mongo DB (BSON) [12,25], Perservere, Thru DB, Orient DB, Terrastore, RavenDB, and Jackrabbit [26].…”

Section: Document-based Nosql Storesmentioning

confidence: 99%

“…The key-value store's simplicity makes it ideal to fast and scalable retrieval of the values needed for the applications activities like retrieving product names or managing user profiles or sessions [13] and Telecom directories [20]. Examples of this type of NoSQL DBMS' are: Redis, Voldemort (LinkedIn), Riak, and Berkeley DB [13,25].…”

Section: Key-value Nosql Storesmentioning

confidence: 99%

A COMPARATIVE STUDY AMONG THE MAIN CATEGORIES OF NoSQL DATABASES

2020

Al-Azhar Bulletin of Science

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Relational databases are usually used for data storage and retrieval. They are suitable for limited data volume. But when it comes to Bigdata, we need to use more flexible databases that satisfy the need to handle semi-structured and unstructured data. These databases are called NoSQL (Not only SQL) databases. This type of database was developed to interact with data of large volumes. NoSQL databases provide many features such as scalability, availability, replication models, file sharing, and schema-free. This paper's main purpose is to present a comparative study of the five main categories of NoSQL databases; key-value stores, document stores, column family stores, graph stores databases, and object store NoSQL systems. Also, it discusses the famous database management systems for each one of these five categories. The comparison criteria used are performance, scalability, flexibility, complexity, and functionality. Moreover, this paper presents an overview of big data concepts. It briefly discusses the SQL databases versus NoSQL databases in terms of their high-level characteristics. Furthermore, this paper emphasizes the advantages and disadvantages of NoSQL databases. It illustrates the query languages in both SQL and NoSQL databases and represents the most common uses for each category to help users choose the most convenient DBMS for their organization.

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Complex SQL-NoSQL Query Translation for Data Lake Management

Nurhadi¹,

Kadir²,

Surin³

2022

Journal of Computer Science

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A data lake refers to an extremely large data resource or repository. Data lakes store large amounts of data and use advanced analytics to pair data from multiple sources with different types of structured, semistructured, and unstructured information. NoSQL databases such as Mongodb, Redis, Neo4j, and Cassandra are nontabular and they store data differently rather than use relational tables. NoSQL databases come in many forms, mostly documents, key values, wide columns, and graphs based on their data model. NoSQL gives less complicated scalability and higher overall performance as compared with traditional relational databases. NoSQL databases can store different types of data, but they cannot fully support Automation, Consistency, Isolation, and endurance (ACID) features, i.e., trigger functions in multi-transaction management, because a NoSQL database uses a non-relational database system. Thus, an interpreter is necessary for SQL-to-NoSQL queries. We used ANTLR (ANother Tool for Language Recognition), which has five main stages: The input SQL query, the tokenizer, the parser, the parser tree, and lastly the generation of the query results in NoSQL. The tool gave users to write a flexible multi-pass language parser that is expected to solve problems in querying complex ACID functions and other problems in complex queries in NoSQL databases. In the measurement, analysis, and evaluation of the translation results through the comparison of each NoSQL criterion against a Relational Database Management System (MySQL), the scores obtained were as follows. The performance criterion achieved the highest score (98.40%) by the MongoDB database, followed by scalability (97.40%) and accuracy (97.00%). The criterion with the lowest score was complexity (91.65%).

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A Qualitative Comparison of NoSQL Data Stores

Cited by 3 publications

References 14 publications

A New Algorithm for Storing and Migrating Data Modelled by Graphs

A New Algorithm for Storing and Migrating Data Modelled by Graphs

A COMPARATIVE STUDY AMONG THE MAIN CATEGORIES OF NoSQL DATABASES

Complex SQL-NoSQL Query Translation for Data Lake Management

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