What Is NoSQL? (Definition, Types, Uses and Advantages)
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Database management solutions offer users flexible ways to store data, discover relationships and grow their operations. For programming and technology professionals, how they choose to store data can depend on their business needs, the data types they use and their unique access requirements. NoSQL databases can be a powerful solution for companies that have outgrown the capabilities of relational database modeling. In this article, we explain what a NoSQL database is, list its common types, describe the most common uses for NoSQL applications and offer some advantages of managing data using these systems.
What is a NoSQL database?
In computer programming, a NoSQL database is a storage management system technical professionals use to manage, store, organize, query and understand their data. NoSQL is not a relational database, meaning it has a nonlinear structure and can't be queried using SQL, a common programming query language. Primarily used by large companies, NoSQL databases can support most cloud application requirements and are often easier to scale and access globally. When operating business applications, NoSQL can offer a variety of benefits to technology companies or others who experience rapid growth or expansion.
NoSQL vs. RDBMS
NoSQL database management systems differ from relational database management systems (RDBMS) like SQL because they meet different application requirements and use cases. Companies may need to use a combination of systems to meet the needs of their varying business purposes. Here are some key differences between the two systems:
With most relational databases, data exists in tables that programmers can query to determine relationships. Users have to predefine each relationship before they can input their data into a table. While this can make it an effective storage and management solution, it can't always accommodate every type of data.
In contrast, nonrelational, or NoSQL databases aren't schema-based. This means they don't use a schema table to describe the individual elements present in the database and don't have to be updated every time there's a change in data type. Depending on a business' needs, this can make them more flexible and a more appropriate option for certain types of data storage.
Another key difference between the two data storage methods is how they scale. While both SQL and NoSQL databases are scalable, SQL databases scale vertically. This means that in order to increase the capacity of the database system, companies need to add more power by adding central processing units (CPUs) and random access memory (RAM) to their existing machine. This can add cost and require companies to come up with additional physical storage solutions.
In contrast, NoSQL databases scale horizontally. With horizontal scaling, users partition the data across nodes. Adding additional nodes reduces the load of each and can increase the reliability of the entire system. While vertical scaling can encounter limits based on a single machine's capacity, horizontal scaling offers the option of adding more machines in multiple locations. In this way, NoSQL databases can offer helpful solutions to large-scale companies that need to access data globally, remain flexible in their strategies or scale quickly without a lot of downtime.
Another difference between the two database types is in how they organize information. Most SQL databases use tables to store and organize data. Many NoSQL databases are document-based, although they might also include graphs, key-value pairs or columns. The organizational systems companies use often matter as the company grows, and non-relational databases can offer performance optimizations when scaled. This is because they often use less memory, can quickly overcome single faults and can better accommodate modern needs, like cloud-based or object-oriented programming requirements.
Types of NoSQL databases
There are four primary NoSQL database types. The kind programmers choose can depend on their business needs, the type of data they're storing and the methods they use to retrieve information from their database. The four most commonly used types are:
A key-value store, or key-value database, can offer users flexibility over traditional relational database models because of its reduced storage needs. This is partly because they don't use input parameters or placeholders to represent optional values, the way most SQL databases do. Key-value stores hold associative arrays and dictionaries, which are abstract data types. These data types operate in pairs, making each key in the database unique.
Like relational databases, wide column stores, or wide-column databases, use columns, rows and tables, but, unlike relational databases, column names and formats don't have to be the same in every row. They're a two-dimensional subclass of a key-value store. Cassandra and Bigtable are two common types of wide-column databases.
Also called a document store, a document-oriented database keeps information in a document format. The data in the document is semi-structured, with the internal construction of the document dictating how it extracts metadata, which provides additional information about the data in the document. You can encode documents with different systems, like JSON, XML and even some binary formats, like BSON.
Graph databases are like document stores, but the graph format can make it easier to see the relationship between data points. Graph database units comprise vertices, called nodes, and edges, with the edges representing the relationships between the nodes. They can be a useful resource for storing inter-connected data, and often make it easier to visualize relationships and query data than some of the other NoSQL database models do. You can also use graphs to link documents and navigate between databases quickly.
Related: Common SQL Joins Interview Questions
Uses for NoSQL
Companies can use NoSQL databases for a variety of use cases. Typically, they employ them when SQL databases can't meet their all of their business needs, but often they use both SQL and NoSQL in their operations.
Here are some instances where a NoSQL database can offer more value to users:
Fast-paced projects: Companies that use agile development techniques might use NoSQL databases to help with sprints, code pushes and rapid iterations. NoSQL models can give them more control over their data's structure and facilitate faster turnaround times.
Multiple data forms: Because NoSQL databases can support multiple data forms in a single document, they can be a good option for companies storing many data types at once.
Storing large amounts of data: Large-scale operations, including global operations, can benefit from NoSQL's horizontal scaling. Dispersing data across nodes rather than relying on a single machine can keep data accessible, high functioning and less susceptible to wide-scale failure as the result of a single fault.
High traffic: Companies who develop highly trafficked sites might use NoSQL databases in their operations because they don't require downtime or experience decreased performance if a single aspect of the system needs adjusting.
Advantages of NoSQL
Here are some advantages of using NoSQL:
Compatible: Most NoSQL databases are compatible with cloud-based applications and agile development techniques.
Scalable: NoSQL can be easier to scale, offering higher levels of performance to large or rapidly growing companies. This makes it a great option for large companies managing huge data quantities that also need to deliver personalized and high-quality user experiences.
Accessible: Another benefit of NoSQL is its accessibility. Billions of users can access the data globally.
Adaptable: NoSQL data solutions arose to respond to needs SQL databases couldn't meet. They can be a great option for companies who have outgrown relational database modeling or who have unique use cases.
Accommodating: An added advantage of NoSQL databases is their ability to accommodate multiple data types. Unlike SQL databases, they don't need to be taken down when the structure of their data changes.
Please note that none of the companies mentioned in this article are affiliated with indeed.
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