NoSQL Basics: Features, Types, and Examples – Spiceworks News and Insights

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NoSQL databases are non-tabular and handle data storage differently than relational tables.

NoSQL databases are defined as non-tabular databases that handle data storage differently as compared to relational tables. NoSQL database types are classified according to the data model, and the popular types include document, graph, column, and key-value. This article details the features, types, and examples of NoSQL.
NoSQL databases are non-tabular and handle data storage differently than relational tables. These databases are classified according to the data model, and popular types include document, graph, column, and key-value.
Non-relational in nature, the core function of NoSQL is to provide a mechanism for storing and retrieving information. Data modeling occurs using means not included under the tabular relations associated with relational databases.
The earliest form of NoSQL databases existed in the 1960s. However, they were not known as NoSQL then; this moniker only originated once such databases gained popularity after the turn of the millennium.
Critical advantages of NoSQL include flexible schemas and easy scalability, even when dealing with large numbers of users and massive data volumes. This class of databases is helpful in the Big Data space and for real-time web applications.
The term ‘NoSQL’ may have initially meant ‘non SQL’; however, it became more widely accepted as ‘not only SQL’ with time. This highlights the fact that NoSQL is capable of supporting SQL-like query languages.
The key features of NoSQL include simple design, seamless horizontal scalability, and granular availability control. Swift operations are also an advantage of NoSQL, brought about by the difference in data structures compared to the defaults used in relational databases.
NoSQL occasionally goes easy on consistency in exchange for higher speed, partition tolerance, and availability. This flexibility of NoSQL data structures allows these databases to solve various business problems.
While NoSQL has several advantages (which we’ll discuss in the next section), it also comes with a few disadvantages. For instance, NoSQL databases feature a narrow scope as they are chiefly designed for data storage. For applications such as transaction management, relational databases are usually a better choice.
Apart from this, the lack of widely-adopted business standards for NoSQL often means that two independent database systems are unequal. This can also introduce obstacles in managing more extensive NoSQL databases, a challenge not made lighter by the lack of well-known, widely used GUI mode tools. Finally, specific NoSQL database systems store data as JSON, leading to the creation of large documents.
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While relational databases are still used for a variety of business functions, the use of NoSQL databases is gaining popularity. Today, companies across industry verticals rely on NoSQL databases to handle their real-time cloud, web, and big data applications. 
The key features of NoSQL databases are:
Relational databases usually require data to be placed in table form before they can access and analyze it. They are typically rigid in data handling, requiring information to be well-structured before it can be ingested.
This is not a limitation in the case of NoSQL, whose compatibility with multiple data models makes it highly flexible regarding data handling. It can ingest unstructured, semi-structured, and structured data, all with equal speed and ease. This is especially useful as specific applications require specific data models.
NoSQL databases find favor with architects and developers as they aim to handle various Agile development requirements swiftly and efficiently. By supporting numerous data models, NoSQL allows the same data to be used in different types of data models without the need to manage separate databases.
Relational databases are undoubtedly scalable. However, their traditional server-client architecture makes both horizontal and vertical scaling a resource-intensive process.
In contrast, NoSQL solutions can offer a serverless, peer-to-peer architecture with consistent properties among all nodes. This allows for simplified scalability, making NoSQL highly adaptable for cloud applications. Performance is also improved, enabling more excellent read and write speeds and continuous availability.
NoSQL databases also use sharding to scale horizontally. Data is partitioned and placed on numerous machines in this process to preserve its ordering. The high flexibility of NoSQL allows it to handle massive data volumes efficiently.
Finally, the auto replication capability of NoSQL also helps ensure high availability, as the solution responds with replication to the last-known consistent state in case of any failure.
Cutting-edge NoSQL databases can ensure data distribution at a global scale. This is achieved through multiple cloud regions and data centers for read-and-write operations across several locations.
This contrasts relational databases, which generally rely on centralized, location-dependent applications for read and write operations. By distributing multiple copies of data to ensure that information is as close as possible to where it needs to be, globally distributed NoSQL databases ensure minimal wait times.
Finally, NoSQL databases are robust and feature minimal downtime. Business continuity is bolstered through serverless architecture and the creation of multiple copies of data to be retained across nodes. In case of a node malfunction, a different node will grant access to its copy of the data.
In a word, no. NoSQL is better for specific use cases, while relational databases are well-suited for others. Making a choice depends on the particular needs of the adopting organization. For certain use cases, enterprises can even deploy both database types together, as these solutions are capable of complementing each other.
Users dealing with numerous data types at once usually prefer NoSQL. This solution is well-suited for creating powerful cloud and web applications for a widely distributed, swiftly expanding audience. Its flexibility, multi-modality, scalability, availability, and high distribution make NoSQL ideal for such applications.
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NoSQL databases come in five main variants, each with its advantages and limitations. No ‘ideal’ variation exists; enterprises must select database types based on their business needs.
The types of NoSQL databases are:
In this NoSQL database type, as the name suggests, information is stored as a key and a value. For instance, a key-value pair can consist of a key ‘City’ with an associated value ‘Chicago’.
Data storage takes place in the form of a hash table with unique keys. Values can be stored in several formats, including string, JSON, or BLOB. This variant is tailored for handling heavy data loads.
Developers often rely on key-value NoSQL for storing schema-less data. Companies leverage this simple NoSQL database for applications such as dictionaries, collections, and associative arrays. A commonly-seen use of key-value pairing is for shopping carts on ecommerce websites.
Key-value pair NoSQL is conceptually based on hash tables using a unique key and a pointer to a specific data item. Key-value storage can include entirely new sets of key-value pairing captured as objects. However, despite featuring the most straightforward implementation process, it is inefficient for querying or updating partial values.
Key-value NoSQL solutions include Dynamo, Redis, Riak, Tokyo Cabinet/Tyrant, Voldemort, Amazon SimpleDB, and Oracle BDB.
Column-based NoSQL databases operate on columns, with each column being treated independently. Due to ready data availability in the column form, this database type is preferred for high-performance delivery in cases involving aggregation queries such as COUNT, SUM, MIN, and AVG.
Enterprises leverage column-oriented NoSQL databases for storing and processing large data volumes distributed across numerous computers. Contiguous storage of values in single-column databases is a vital feature of this type of database.
Column arrangement takes place based on the column family, and while keys exist in this NoSQL type, they point to several columns. Such databases are mainly used for managing applications such as business intelligence, data warehouses, library card catalogs, and CRM.
Examples of column-based NoSQL databases include Cassandra, HBase, and Hypertable.
In a document-oriented NoSQL database, data storage and retrieval occur in the key-value pair format. However, values are stored in the document form using either the JSON or the XML format. The database understands the values and allows them to be queried more efficiently.
Document NoSQL databases store data such as JSON objects. Users need not define the data, thus enhancing flexibility. Applications of document-oriented NoSQL databases include blogging platforms, CMS systems, ecommerce, and real-time analytics.
At the most superficial level, this NoSQL model uses versioned documents that are collections of key-value databases. Formats such as JSON are used for the storage of these semi-structured documents. 
Essentially, document-oriented NoSQL databases are the next level of key-value databases, enabling each key to have an associated nested value. However, this database type is not ideal for complex transactions requiring multiple queries or operations against differing aggregate structures.
Let us look at an example of this database type in action. In a relational database, a collection of articles from research journals would be split with the text in one table, the information on researchers in another, and perhaps citations used in each article in a third. However, in a document-oriented NoSQL database, the entire article and all associated data would be stored as a single entity. This reduces the complexities related to data operations.
Document-oriented NoSQL database solutions include MongoDB, CouchDB, Riak, Amazon SimpleDB, and Lotus Notes.
Graph-type NoSQL databases store entities along with the relationships among them. Each entity is treated as a node during storage, with the relationships stored as edges. An edge establishes relationships among the nodes. Each node and edge is assigned a distinct identifier.
While loosely connected tables are a vital aspect of relational databases, graph NoSQL databases are multi-relational. Here, establishing relationships is a swift process as the data is already present and need not be calculated. This type of database is mainly leveraged for applications such as logistics, social networks, and spatial data analysis.
This database model is not held back by the disadvantages associated with the row-and-column tables and inflexibly structured SQL. Instead, it uses a flexible graph model that one can scale across numerous systems.
The two components of Graph-type NoSQL databases are the node and the edge. Each node is an entity, for instance, the people in a social network application. On the other hand, the edge is the relationship among entities. Every relationship has its properties and is typically represented by a line. An edge usually features a direction; for instance, an arrow indicates superiority in an organization’s hierarchy.
Large-scale deployments of graph NoSQL can become significantly complicated due to many entity types and relationships. Graph-based NoSQL database solutions include Neo4J, Infinite Graph, and FlockDB.
Finally, multi-model NoSQL databases are built for handling numerous data models against one integrated backend. Typically, database management systems support one data model; however, multi-model databases can store, query, and index data from multiple models.
Users rely on multi-model databases for the modeling advantages associated with polyglot persistence without the need to combine various models. This flexibility enables data storage in varying ways, resulting in swifter Agile programming and the minimization of data redundancy.
A key feature of multi-model databases is data transformation among formats; for instance, one can quickly transform data stored in a JSON format into XML. Other advantages of multi-model databases include consistency of inter-model data due to a shared backend and ACID compliance leading to higher fault tolerance.
Examples of multi-model NoSQL database solutions include OrientDB, ArangoDB, and MarkLogic Server. This type of NoSQL database is well-suited for complex projects requiring several data views.
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We have already looked at the different NoSQL solutions in the previous section. Now, we’ll go through the examples of NoSQL in detail.
MongoDB is one of the foremost open-source NoSQL systems. It is a document-oriented database that uses dynamic schemas to store JSON-like documents. This database solution features a flexible data model, enabling users to store unstructured data. Users can also access full indexing support and replication through intuitive API.
MongoDB’s popularity among developers includes its flexible data model and intuitive API. Other key features of this solution include:
This web-oriented NoSQL database is open-source and stores documents using the JSON data exchange format. The CouchDB API is based on HTTP, and the solution uses JavaScript for indexing, transforming, and combining documents. Unlike relational databases, it uses a schema-free data model to simplify record management across endpoints.
CouchDB is backed by an active dev community that constantly works to enhance its ease of use and ensure a web-first approach. Additionally, its open-source nature minimizes the chances of ‘vendor lock-in’, an advantage not seen in the case of proprietary NoSQL solutions.
CouchDB is free to use and can integrate swiftly with existing data management infrastructure. More user control also translates to greater flexibility and applicability for varying business needs; the solution is optimized for general-purpose document storage, efficient data synchronization, and even ‘offline-first’ setups. Organizations rely on CouchDB for creating scalable, reliable, and durable infrastructure.
This proprietary NoSQL database supports key-value and JSON table data models and is built to operate either on-premise or over the cloud. Developers leverage Oracle NoSQL Database Cloud Service to create applications using column, document, and key-value data models.
The key features of Oracle NoSQL Database include:
Built by Basho Technologies, this key-value store NoSQL database is open-source and written in Erlang. It features automatic data distribution and built-in fault-tolerance replication, setting its performance apart from some of the other solutions in this space.
This NoSQL database solution is web scalable and based on the Dynamo database system. It is highly distributed, scalable, and reliable in varying business environments. The solution is built for the web, mobile, and cloud. Besides its free, open-source version, Riak has a commercial version for enterprise use.
Riak’s fault tolerance stems from its high distribution across several nodes and masterless implementation that help avoid a single point of failure. It is ideal for distributed cloud data architectures and high-volume read-and-write applications. Users generally prefer Riak for building cloud file systems.
InfiniteGraph is a highly specialized graph database focused on graph data models. It is implemented in Java and is ideal for spotting hidden trends in massive, complex, and highly connected datasets. This NoSQL database solution is distributed in nature and implemented in C++ and Java.
InfiniteGraph is scalable, cross-platform, cloud-powered, and built for handling high throughput levels. It is capable of swiftly and efficiently performing queries that are otherwise difficult to execute, such as locating all paths or the shortest path connecting two items.
InfiniteGraph is the preferred NoSQL solution for services and applications that involve solving graph problems in business environments. Its ‘DO’ query language supports complex graph and value-based queries. Apart from graph databases, it can also handle complex object queries. This solution is popularly used in industry verticals, including governance, healthcare, telecommunications, cybersecurity, finance, manufacturing, and networking.
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Relational databases are widely relied on today for their consistency. However, they are not always the ideal solution for high performance, especially for applications that engage in frequent data storage and processing at a massive scale. High performance, ease of access, and high scalability are the key reasons for the increased popularity of NoSQL databases.
However, NoSQL is a robust solution that adds tremendous value to existing database standards; it is not a catch-all replacement for relational databases. It exchanges consistency and reliability for scalability and performance, making it a specialized solution that a relatively limited number of applications can rely on.
Regardless, this cutting-edge database solution comes in numerous types and is backed by several popular solutions developed by experts and used in many verticals.
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