![I've (for the sake of research) recently experimented with NoSQL databases such as MongoDB, Cassand ra, and
CouchDB. NoSQL is not designed for server-side querying, as far as I am aware. There is no join procedure, thus
embedding records into one collection is ineffective. It is not suited for use in commercial transactions.Aggregation
framework for these NoSQL databases currently lacks performance adjustment. And so forth. It is suitable for
applications that do not require numerous transactions but require a large amount of data storage. Also, there is no
common, standardized query language which is the industry standard as there is in SQL for relational databases.
NoSQL databases have been existed since the 1960s, but it wasn't until the early twenty-first century that
businesses began to embrace them, particularly to manage huge data and real-time online and cloud
applications. Since then, the NoSQL database has grown in popularity and utility, albeit relational databases
remain useful (11).
2.2. Data Model
Data models describe how a database's logical structure is represented. Data Models are basic elements in a DBMS
for introducing abstraction. Data models determine how data is linked to one anotherand how it is handled and stored
inside the system. The very first data model might be flat data-models, in which all data is held on the same plane.
Earlier data models were not as scientific; therefore, they were prone to duplication and update abnormalities (12).
The following are the NoSQL database fields and the standard data model:
2.2.1. Key-Valued:
The key-value database may be viewed as an associative array, which is a generalization of a standard array, i.e.
A["anything"]= "something," where "anything" is the key and "something" is the value. This is a strong notion; for
example, an in-memory key-value database such as Redis may act as a distributed hash table, expanding the RAM
concept. You may use this simple yet effective program to pool (cluster) the RAM of numerous commodity PCs to
build a massive main-memory to offer your application a significant speed increase. Of course, there are additional
factors to consider, but let's avoid the gory specifics (13).
2.2.2. Column Family:
Column Family databases may be viewed as C structure objects with an arbitrary number of fields. This class includes
Cassandra and HBase. These can alternatively be viewed as "relaxed" variations of typical RDBMS databases with
the primary purpose of scalability. This work is suitable for processing column-oriented data.
2.2.3. Document:
The structure of a document database and a key-value database is quite similar; however, the value of a document
database is semantic and is saved in JSON or XML format. Furthermore, document databases may typically have a
Secondary Index to value to help with the higher application, although key-value databases cannot (14).
2.2.4. Graph:
Graph databases model the most complicated data by making data relationships first-class residents of the system.
These databases can perform fast graph focused queries such as path traversals by optimally modelling the data as
graphs, multi-graphs, or hyper-graphs. So, they are fantastic for friend-of-a-friend questions. Some of them,
interestingly, employ key-value databases as an underlying technology to improve speed and accomplish scalability.
Some good examples include Neo4j and Hypergraph db.](https://image.slidesharecdn.com/report1-220629203912-a084f9eb/85/Report-1-0-docx-3-320.jpg)
Report 1.0.docx
- 1. Abstract: Growing firms confront a variety of obstacles and possibilities that need long-term planning. What worked a year ago may no longer be the greatest strategy today.Similarly, while developing a new enterprise.NET application, you must considercarefully and select the best technology for the job. If you make the wrong decision, it will have a long-term impact on your entire firm. Choosing the correct database technology is one ofthe factors that will influence how your application is designed (1). With the growth of the Internet and cloud computing, databases must be able to store and process large amounts of data efficiently, as well as provide high performance while reading and writing, therefore the classic relational database is facing several new issues. Using a relational database to store and query dynamic userdata has proven insufficient, particularly in large-scale and high-concurrency applications such as search engines and social networking sites. In this instance, a NoSQL database was constructed. This paper discusses the history, fundamental features,and data model of NoSQL and finally some renowned NoSQL databases are being discussed to help corporate firms decide which database to use (2). Key words: NoSQL; Key-value; Column Oriented; document; database; Big Data 1. Introduction: The fundamental question ofwhy do we need NoSQL database when we can use SQL database can be answered using the analogy of different types ofscrewdrivers. Almost all screwdrivers do the same job i.e., tightening and loosing the screws. So why don’t we use the best one eliminated the rest? Standard/slotted screws are less expensive to produce. They're difficult to use with electric screwdrivers, though,since the driverkeeps falling out.Phillips-head screwdrivers are preferable for this, although they can peel out if the metal isn't strong enough. And so forth. Each type of screw has a purpose and is beneficial for a variety of applications.It's the same with a lot of otherforms of technology.There are tradeoffs between complexity and ease, cost to create vs. cost to use, and so on (3). Likewise different types of databases are used for different purposes: A relational database is intended to meet the needs ofthe data. You create a schema that ensures well-formed data. When you know that the data is properly structured, it is easy to create queries (4). NoSQL databases are more flexible, allowing you to begin storing data in a variety of formats. These are more versatile in terms of data format, but there is no assurance ofdata consistency therefore writing queries is more difficult. Furthermore, the database architecture is centered on yourquery needs rather than the data requirements. So, you must first assess the queries that must be executed (5). With the evolution of database technology over the ages many different database systems have emerged. With the advancements in internet technologies and hardware components, the databases are now high in demand than ever before for the following reasons: Connectivity and centralization: Most DBMS only support one form of database,such as SQL, NoSQL, or Key-value. This means that you'd require a graphical userinterface (GUI) for each of these databases in order to handle them as an administrator. A good DBMS should help consolidate all of these databases and operate as a data warehouse. Processing in memory: An in-memory database stores all data in a computer's main memory, or RAM. A typical database pulls information from hard discs. When it comes to data processing,in-memory databases have a significant performance advantage. They are significantly quicker than traditional databases because they utilize fewer CPU instructions and eliminate the time required to read data from a drive. Performance and distributed computing:
- 2. When it comes to RDBMS, huge parallel computing has numerous major advantages. A. It can help to calculate a greater quantity of data simultaneously, making it considerably quicker. B. Users are not required to access the database in the same network environment. Secure and IAM-enabled Any DBMS must have Identity Access Management. Despite the fact that most systems are not inherently collaborative. This implies a significant knowledge gap between the administrator and the end users. The finest solutions should facilitate cooperation while maintaining a high level of security. Excellent UI/UX The majority of DBMS pay no attention to UI/UX. The best should appeal to customers from all backgrounds,since data consumers nowadays might range from company managers to data analysts.A great user interface should assist achieve significantly higher efficiency than an IT-dependent solution (6). Although relational databases have a prominent position in the data storage sector,when confronted with the following criteria, they have certain inherent limitations: Poor Interface: SQL databases have very complexinterface which makes it difficult for the user to interact. Cost Inefficient: SQL databases cost huge amount which make themvery expensive. Partial Control: Due to some best kept secret business policies SQL databases grants limited control to its users. Security: Due to huge amount of sensitive data being stored on SQL databases it is at huge risk concerning security (7). A variety of different types of databases have emerged to address the aforementioned demands. Because these novel databases differ significantly from typical relational databases, they are referred to as "NoSQL" databases (8). 2. Characteristics, data model, and categorization 2.1. Features of NoSQL Database The main advantages ofNoSQL database are following: 1) Support for Multiple Data Models; 2) Easily Scalable via Peer-to-Peer Architecture; 3) Flexibility: Versatile Data Handling; 4) Distribution Capabilities; 5) Zero Downtime (9). One of the key characteristics of NoSQL database and probably the reason of its development is how to handle diverse data structures before writing it Thus, NoSQL refers to a wide range of databases that can readily store and manage enormous amounts of semi-structured and unstructured data. They can manage a large volume of reads and writes while also expanding data horizontally. Even if there is no logical grouping, NoSQL allows us to arrange any type of stored data. It provides several benefits through the use of various types of databases,such as hybrid cache stores, graph databases, and so on (10).
- 3. I've (for the sake of research) recently experimented with NoSQL databases such as MongoDB, Cassand ra, and CouchDB. NoSQL is not designed for server-side querying, as far as I am aware. There is no join procedure, thus embedding records into one collection is ineffective. It is not suited for use in commercial transactions.Aggregation framework for these NoSQL databases currently lacks performance adjustment. And so forth. It is suitable for applications that do not require numerous transactions but require a large amount of data storage. Also, there is no common, standardized query language which is the industry standard as there is in SQL for relational databases. NoSQL databases have been existed since the 1960s, but it wasn't until the early twenty-first century that businesses began to embrace them, particularly to manage huge data and real-time online and cloud applications. Since then, the NoSQL database has grown in popularity and utility, albeit relational databases remain useful (11). 2.2. Data Model Data models describe how a database's logical structure is represented. Data Models are basic elements in a DBMS for introducing abstraction. Data models determine how data is linked to one anotherand how it is handled and stored inside the system. The very first data model might be flat data-models, in which all data is held on the same plane. Earlier data models were not as scientific; therefore, they were prone to duplication and update abnormalities (12). The following are the NoSQL database fields and the standard data model: 2.2.1. Key-Valued: The key-value database may be viewed as an associative array, which is a generalization of a standard array, i.e. A["anything"]= "something," where "anything" is the key and "something" is the value. This is a strong notion; for example, an in-memory key-value database such as Redis may act as a distributed hash table, expanding the RAM concept. You may use this simple yet effective program to pool (cluster) the RAM of numerous commodity PCs to build a massive main-memory to offer your application a significant speed increase. Of course, there are additional factors to consider, but let's avoid the gory specifics (13). 2.2.2. Column Family: Column Family databases may be viewed as C structure objects with an arbitrary number of fields. This class includes Cassandra and HBase. These can alternatively be viewed as "relaxed" variations of typical RDBMS databases with the primary purpose of scalability. This work is suitable for processing column-oriented data. 2.2.3. Document: The structure of a document database and a key-value database is quite similar; however, the value of a document database is semantic and is saved in JSON or XML format. Furthermore, document databases may typically have a Secondary Index to value to help with the higher application, although key-value databases cannot (14). 2.2.4. Graph: Graph databases model the most complicated data by making data relationships first-class residents of the system. These databases can perform fast graph focused queries such as path traversals by optimally modelling the data as graphs, multi-graphs, or hyper-graphs. So, they are fantastic for friend-of-a-friend questions. Some of them, interestingly, employ key-value databases as an underlying technology to improve speed and accomplish scalability. Some good examples include Neo4j and Hypergraph db.
- 4. 2.3. CAP Theorem and NoSQL According to the CAP theorem, a distributed system cannot be consistent, available, and partition tolerant at the same time. When we needed to store more data or expand our processing power in the past, we had two options: grow vertically (buy more powerful computers) or further optimize the existing code base. However, with advancements in parallel processing and distributed systems, it is more typical to extend horizontally, or to have several computers performing the same work in parallel. In the Apache project, we can already witness a slew of data manipulation tools like as Spark, Hadoop, Kafka, Zookeeper, and Storm. However, in order to properly select the tool of choice, a fundamental understanding of the CAP Theorem is required. The CAP Theorem states that a distributed database system can only have two of the three characteristics: consistency, availability, and partition tolerance (15). The classification of NoSQL according to CAP theorem is as follows (16): Consistency and availability (CA): Part of the database is unconcerned with partition tolerance and instead relies on the replication strategy to assure data consistency and availability. CA-related systems include the standard relational database, Vertica (column - oriented), Aster Data (Relational), Greenplum (Relational), and others. Consistency and partition tolerance (CP): Such a database system stores data on remote nodes while also ensuring data consistency, although support is insufficient for availability. BigTable (Column-oriented), Hypertable (Column-oriented), HBase (Column-oriented), MongoDB (Document), Terrastore (Document), Redis (Key-value), Scalaris (Key-value), MemcacheDB (Key- value), Berkeley DB are the primary CP systems (Key-value). Availability and partition tolerance (AP): Such systems provide availability and partition tolerance largely through consistency, as demonstrated by AP's system: Voldemort (Key-value), Tokyo Cabinet (Key-value), KAI (Key-value), CouchDB (Document-oriented), SimpleDB (Document-oriented), Riak (Document-oriented), Riak (Document-oriented), Riak ( (Document- oriented).
- 5. 3. Analysis of popular NoSQL databases 3.1. MongoDB MongoDB is an open source NOSQL database written in C++ language. 3.1.1. Structure and Data model MongoDB (17) is a document database,not a relational database management system. That is, records are saved as documents (usually JSON) rather than tuples. As a result, data modelling for MongoDB necessitates a more object - oriented approach than data modelling for an RDBMS. Both composition and association relations, for example, can be described using mapping tables in an RDBMS; it is up to the application to enforce the exact nature of the connection - composition or association. However, in the case of a document database such as MongoDB, a composition relation must be enforced at the document level using nested/contained objects. 3.1.2. Query operators Query operations in mongo dB can be used for the following purposes (18): Comparison: For e.g., “$eq” matches values that are equal to a specified value; “$gt” matches values that are greater than a specified value; “$lt” matches values that are less than a specified value, etc. Logical operations: For e.g., “$and” joins query clauses with a logical AND returns all documents that match the conditions of both clauses; “$not” inverts the effect of a query expression and returns documents that do not match the query expression; “$nor” joins query clauses with a logical NOR returns all documents that fail to match both clauses; “$or” joins query clauses with a logical OR returns all documents that match the conditions of either clause. Element: For e.g., “$exists” Matches documents that have the specified field; “$type” selects documents if a field is of the specified type. Evaluation: For e.g., “$mod” performs a modulo operation on the value of a field and selects documents with a specified result. Geospatial: For e.g., “geoIntersects” selects geometries that intersect with a GeoJSON geometry. The 2dsphere index supports $geoIntersects. Array For e.g., “$all” matches arrays that contain all elements specified in the query. Bitwise For e.g., “$bitsAllClear” matches numeric or binary values in which a set of bit positions all have a value of 0.
- 6. 3.1.3. Examples of query operations In MongoDB, you must first create a collection before you can create a table. The collection works much the same way that we would build a table when putting data into a relational database. To insert the document in the MongoDB collection, you have to use the insert() method. 3.1.4. Common Usage and market Some of the most common mongo dB usage aur as follows (19): Mobility and Scaling: MongoDB is highly scalable and adaptable, making it ideal for dealing with a wide range of settings. Real time data integration: Data has a lot of value when it is condensed and aggregated into a single perspective, and MongoDB plays an important part in that. Product Catalog:
- 7. There are many attributes to products which are easily stored as an object using MongoDB and can be used to understand the customer better in the digital experience. Some of the world’s renowned companies that use mongo dB includes: eBay, MetLife, Shutterfly, Aadhar, EA among many others. 3.1.5. Enhancements in the platform MongoDB is a highly helpful and easy solution, however for more complicated projects, there may be certain specialized needs that necessitate the usage of a different technology.It increases the complexity of the architecture, yet it may be helpful to the world, the features, and the simplicity with which the features are implemented. Also, it could be more stable. It would be preferable if it were more user-friendly, like Oracle is. In Oracle, for example, building an index is straightforward. It's difficult to achieve that with MongoDB. Performance might be improved. It's quick and reliable, but you can't put every application you want on MongoDB. 3.2. Cassandra Cassandra (20) is an opensource database of Facebook. 3.2.1. Structure and Data model Its qualities are as follows: 1) the schema is extremely flexible and does not require the creation of a database schema at the outset, and adding or deleting fields is quite simple. 2) Support range queries, i.e., range queries for Key; 3) High scalability: a single point of failure does not affect the entire cluster, and linear extension is supported. Cassandra is a distributed database systemmade up of several database nodes; a write operation is replicated to other nodes, and a read request is routed to a specific node. Scalability may be achieved by simply adding nodes to a Cassandra cluster. Cassandra also has a complex data structure and a sophisticated query language. 3.2.2. Query Operation and example The Cassandra Update query is used to update the Cassandra table's data. If no results are given after changing data, this indicates that the data was properly changed; otherwise, an error will be issued.The 'Set' clause modifies column values while the 'Where' clause filters data. Syntax: Update KeyspaceName.TableName Set ColumnName1=new Column1Value, ColumnName2=new Column2Value, ColumnName3=new Column3Value, . . . Where ColumnName=ColumnValue Here is a snapshot of the database before data was updated. After running the following query: Update University.Student Set name='Hayden'
- 8. Where rollno=1; Here is the screenshot that shows the database state after updating data. 3.2.3. Common usage and market Cassandra was used to power the new Actionable Analytics product at Ooyala (21), a company that provides an end- to-end online video platform to corporate companies. All video engagement and analytics reports delivered to clients are aggregated across many dimensions and sliceable by the user's geo (country/region/city), the site/URL where a video is embedded, various advertising parameters, and a variety of additional tags. Cassandra offered the scalability and better write throughput required to support the new analytics offering without the requirement to develop their sharding and replication on top of MySQL. Ooyala manage a big volume of analytics data (> 1B data points per month) with millions of hours of video supplied each month. 3.2.4. Enhancement in the product Cassandra's secondary index is somewhat troublesome and might be improved. Cassandra can improve by having a fuller ecosystemintegrator. Companies, for example, required to deploy extra tools in some maintenance operations to execute duties that were not provided alongside Cassandra. Another limitation of the product is that you cannot drop writes like you do in MongoDB and MySQL, where you may link tables. Cassandra does not provide joins across tables; thus, you must use another tool for this. You must read all of the data and store it in memory before adding the joins. That is an area where they can improve. 4. Conclusion Both SQL and NoSQL database formats store data, but how they do so vary. Outline the application to choose the best database architecture for data structures such schema, relation, scalability, and data size. Migrating from one to the other is expensive and time-consuming, thus the differences should be addressed while designing software. This paper begins by comparing traditional databases with that of NoSQL databases. Then it explains the underlying working of NoSQL database and then finally two different NoSQL databases are discussed. Each type was database has its own pros and cons. NoSQL is a recent technology and many of its dimensions are yet in a phase of development. This paper provides a comprehensive overview to the businesses of the kind of database they should use based on their functionalities.