NoSQL databases, also known as “not only SQL”, are non-tabular databases that store data in a different way than relational tables. NoSQL databases can be classified according to their data model. There are four main types: document, key-value, and wide-column. They offer flexible schemas that scale well with large data sets and high user loads.
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What is a NoSQL Database?
People use the term “NoSQL” to mean any non-relational data source. Some people believe that “NoSQL” is an acronym for “non SQL”, while others think it means “not only SQL”. However, all agree that NoSQL databases are databases that store data other than in relational tables.
NoSQL can be used to refer to any alternative to traditional SQL databases.
NoSQL databases can be very different from SQL databases. All of them use a data structure that is different from the row-and-column model used in relational database management systems (RDBMSs).
However, NoSQL databases can be quite different from one another.
What NoSQL Databases Have in Common
Why Use NoSQL Databases explains that NoSQL databases were created during the Internet age to address the requirements of large-scale web applications that handle huge amounts of traffic and data.
NoSQL technology is being used by companies to address a wide range of business needs. It also saves money compared to maintaining a relational database. NoSQL databases are flexible in their schema models and can scale horizontally across multiple servers. This makes them attractive for large data volumes, or applications loads exceeding the server’s capacity.
These are the reasons NoSQL is so popular:
- NoSQL databases are able to develop faster than SQL databases.
- A NoSQL database makes it easier to manage and evolve the structure of multiple data types.
- A SQL database cannot handle the large amount of data required by many applications.
- SQL cannot handle the traffic volume and downtime required. It is easier to support new application paradigms.
NoSQL Database features
Every NoSQL database is unique. Many NoSQL databases contain the following features at a high level:
- Flexible schemas
- Horizontal scaling
- Quick queries thanks to the data model
Differences in the Types of NoSQL Databases
Here are some NoSQL database types:
- Document databases
- Key-value stores
- Column-oriented databases
- Graph databases
A document library stores data JSON or BSON XML documents (not Word or Google docs). Documents can be nested in a document database. You can index specific elements to make it easier to query.
Documents can be stored in a format that is closer to the data objects in applications. This means that less translation is needed to access the data. When moving between storage and applications, SQL data often needs to be assembled and disassembled.
Developers love document databases because they can modify their document structure as necessary to fit their applications. They can also shape their data structures to meet changing requirements. Because data is like code, developers can control it. This flexibility speeds up development. SQL databases may require administrators to modify the database structure.
Most document databases that are most popular have a scale-out architecture. This allows for easy scaling of data volumes and traffic.
Ecommerce platforms, trading platforms, and mobile app development across all industries are some examples of use cases.
A comparison of MongoDB and PostgreSQL provides a comprehensive analysis of MongoDB (the leading NoSQL database) and PostgreSQL (one of the most well-known SQL databases).
A key/value store is the simplest NoSQL database. Each data element is stored in a key-value pair, which consists of an attribute (or “key”) as well as a value. A key-value store can be described as a relational database that only has two columns, the key or attribute name (such state) and the value.
These use cases include shopping carts and user preferences.
A relational database stores data in rows, and can be accessed row by row. However, a column store is organized using a collection of columns. When you need to run analytics on a few columns, you can simply read the columns without having to eat memory. The columns are often the same type and can be read faster due to more efficient compression. Columnar databases allow you to quickly add up the value of columns (e.g. adding up sales for the year). Analytics is one example of a use case.
There is no such thing as a free lunch. Columnar databases can be great for analytics but the way they write data makes consistency very difficult. Writes of all columns require multiple write events to disk. This problem is not present in relational databases, as row data are written concurrently to disk.
A graph database is focused on the relationships between data elements. Each element is stored in a node (such as a person in a graph on social media). Links or relationships are used to describe the connections between elements. Connections are the first-class elements in a graph database and are stored directly. Relational databases use data to express relationships, so links are implied.
A graph database is designed to search and capture connections between data elements. It eliminates the overhead of JOINing multiple SQL tables.
Graph queries are not a viable option for many real-world business systems. Therefore, graph databases are often used alongside more traditional databases.
These use cases include fraud detection, social network, and knowledge graphs.
You can see that NoSQL databases have different data structures and applications, even though they share a common umbrella.