Have you ever noticed, tech-knowledgeable person, that SQL is the most fundamental choice and conventional database used for developing an application? The Structure Query Language (SQL), or relational databases, is the remarkable evolution of technology, designed for ad hoc queries, reliable online transactions, and much more. But, SQL comes with inadequate shortcomings compared to NoSQL database, including rigid schema and exploding data volume, which makes this database unsuitable for other high-profile applications and websites.
NoSQL databases are built to overcome these limitations. Unlike SQL relational databases, NoSQL databases are scaled horizontally across thousands of servers in real-time. Its systems can manage and store data, which provides flexibility and highly effective speed on the part of the developers. Most of them are developed by companies like Facebook, Amazon, Yahoo, and Google which allow a significant way to process or store content for websites.
Let’s crackdown to learn everything about NoSQL and when to use NoSQL databases.
What is NoSQL?
A NoSQL database is a non-relational database used for high-volume data storage in JSON documents, other than rows and columns used in relational tables. Typically, NoSQL refers to “non-SQL” or “not only SQL.” Either way, it is an excellent combination of the flexibility of JSON with SQL to meet the data management demands of rapidly scalable businesses. Its databases are distributed systems where various machines work together at a time. These clustered machines replicated all data to improve availability and redundancy. The reason behind NoSQL’s faster deployment is the ability to store large-scale data in a distributed approach.
Moreover, some other vital features of NoSQL systems make them the obvious choice for developers. It includes replication support, data clustering, eventual consistency, and, more importantly, flexible schema.
Brief History of NoSQL Databases
To properly understand the NoSQL database, it is essential to look at alternatives: relational databases, and SQL. In 1970, the relational database was invented by Edgar F. Codd to arrange data into different columns and rows through a specific key that associates with each row. Although SQL databases are phenomenal, they are rigid systems and have a limited ability to process unstructured and complex data.
At the end of the 20th century, relational databases couldn’t meet users’ demands as the internet gaining more popularity than ever. This popularity and demand led to the development of NoSQL. It replaces the rigidity of SQL by providing flexibility and organized storage that helps to manage unstructured data many times faster than SQL. NoSQL was first used in 1998 by Carlo Strozzi, but the name came up when Johan Oskarsson and Eric Evans used it in 2009 to process non-relational databases. NoSQL databases can’t only handle complex and unstructured data with flexibility and speed. Still, they have the ability to manage Big Data that helps organizations like Google, Twitter, and LinkedIn to adopt NoSQL systems.
When to Use NoSQL Database?
Choosing a suitable database for massive data is extremely important as it can simplify your applications. Otherwise, it creates frustration and friction.
Here are the given cases when to use the NoSQL database
- NoSQL database is considered better to manage and model unstructured, semi-structured, and structured data in one database. In addition, it stores data that is close to the objects of the application, and data become like a code, reducing the need for translation. So, it is best to evolve and handle the structure of various forms of data.
- NoSQL database’s scale-out strategy shows a clear way to scale a massive volume of traffic that the database can easily handle. Relational SQL databases cannot control the need for zero downtime and scalability of traffic. The scale-out architecture has the advantage of being able to update a database or change the structure of a database with zero downtime. And the best thing is that scale-out architecture is the most reasonable method to control a tremendous amount of traffic.
- Compared to the SQL database, the pace of development with a non-relational NoSQL database is many times faster. Because this database allows developers to handle the structure of the data so that they can best suit advanced Agile development practices. As it is based on quick iterations, standard code pushes, and sprints. However, in SQL databases, developers change the structure of the database and unload or load the data, which can slow down development.
- Last but not least, the scalability factor of a non-relational database allows serving analytical and transactional workloads from one database. But, a separate data warehouse is needed to support analytics in the SQL database.
If you have such requirements, then I hope you got the idea of when to use the NoSQL database. By the way, MongoDB is one of the most affordable, popular, and accessible ways to start NoSQL databases as it is an open source and document-oriented database.
Types of NoSQL Databases
Non-relational databases can store data that is often horizontally scalable, open source, schema-less, flexible, and use BASE for availability and consistency.
NoSQL stores data into the following categories.
- Document Database
- Key-Value Store
- Graph Database
- Wide Column Database
NoSQL 1: Document Database
Of course, document databases can’t store data in Google Docs and Word documents. Instead, it can store data in XML, JSON, or BSON to manage data for faster queries. Document storage is top-rated among developers because of its significant speed and flexibility, and stored data becomes just like a code that is easy to control for developers. Also, developers can rework their document structure as per the requirements of applications that change over time.
In a document database, documents are stored in a way nearly similar to the objects of the applications. This similarity allows developers to translate less when data is used in the application. Suppose we look at NoSQL’s alternatives, where information is assembled and disassembled while moving between databases and applications. The most widely used cases of document storage are trading platforms, e-commerce platforms, and the development of mobile apps, which shows a clear platform of scalability for traffic and data volumes.
- Most Powerful Database.
- No need for foreign keys.
- Faster querying
- Can’t handle relational data
- Querying is limited to indexes and keys.
NoSQL 2: Key-Value Stores
The key-value store is the simplest type of NoSQL database as each element has key-value pair named key and value. Every key is different, and a value is assigned to it. This set value accepts the XML, string, JSON, etc., corresponding to a particular key. So, it has the ability to deal with massive data. There is a lot of similarity between relational databases and KV stores, but each table in the KV store contains only two columns.
Many KV stores are available; each is different in the configurations of storage v/s memory usage and an adaption of Brewer’s theorem.
- Simple Data storage
- Value can include flexible schemas, XML, JSON
- Because of its simplicity, it is speedy.
- Its best use cases include user profiles, preferences, and shopping carts.
- Complex data is not a good fit for it.
- It has no scanning capabilities.
- It is not considered appropriate for operations other than CRUD (create, read, update, delete).
NoSQL 3: Graph Database
The primary purpose of a graph database is the storage of data that has a graph-oriented structure and also provides index-free adjacency. The index-free adjacency means that each node has a direct pointer to its adjacent elements and no need for index lookups. In a graph database, there is not necessary to describe the types of vertices and edges as it is usually available with the flexible data model.
It is optimized for traversing corresponding data, for example, traversing to a contact list on your social media account to reach the degree of connections. Typically used cases of graph databases are recommendations engines and social networking sites.
- It is highly effective and powerful.
- Able to provide ACID
- Shows results in real-time.
- Although it is scalable, it’s challenging to scale.
NoSQL 4: Wide Column Database
In a relational database, data is stored in rows and read in rows, but data is organized in columns in a column store. It means that whenever you need to run analytics on the columns, you can go through these columns without wasting unwanted data.
Another multi-dimensional aspect of a column database is column families. These column-families databases can manage data in column families in the form of rows with many columns associated with a row key. These families are a group of stored data that can be accessed together.
There are two types of column families:
Standard Column family: Standard column family has a key and value where the key is mapped to a value that is a set of columns. Actually, a standard column family is just like a table, where each KV pair is a row.
Super Column Family: Super column family represents a range of the column. Unlike a relational database, it is like a view of different tables in a database. Suppose that you have an idea of values and columns available for each row; that is the single identifier of different tables and super column families can store them all in one particular place.
- Suitable for semi-structured data
- It contains a multi-dimensional key store
- Did not support relational data
Key Features of NoSQL Database
Here are the most valuable key features that help to give a broader view of when to use a NoSQL database.
The scaling in a NoSQL database enhances the database’s storage as the application users begin to increase. When the number of application user increase, the storage capacity of the application will also increase in order to store and manage new users’ data. This remarkable feature has been difficult to incorporate into the previous database. But, horizontal scaling in the NoSQL database adds the extra nodes to the database to release the load of storage capacity.
Independent of Schemas
The non-relational databases are able to run over without the help of any predetermined schemas, as NoSQL is independent of any schemas. This is more beneficial and efficient to corporate with this feature for developers and programmers who want to handle a large amount of complex data with no schemas.
Support for Multiple Data Models
Relational databases can put the data into columns and tables to be thoroughly analyzed and accessed. But, the multiple data model abilities of non-relational databases make them highly flexible whenever it comes to managing the data. Unlike the relational database, which can handle primarily structured data and is highly rigid, the non-relational database can easily handle structured, unstructured, and semi-structured data.
The multiple data models can handle specific requirements of the application. NoSQL database becomes the fundamental choice for architects and developers as this database can easily control various demands of agile application development. The widely used data models are key-value, wide-column, and graph document.
NoSQL database is developed to distribute data at a universal scale, and it can use different locations involving multiple cloud regions or data centers. Contrastingly, a relational database contains a centralized application, meaning it uses a single site (location dependent), especially regarding writing operations. The main significant benefit of distributed capabilities of the database is that one can manage continuous availability whenever it needs, and data is distributed with multiple copies.
NoSQL databases are beneficial and highly essential for developers and other data industries. These benefits are lightning-fast queries, horizontal scaling, independent schemas, and flexible data models. NoSQL databases have four important types: key-value databases, document databases, graph databases, and wide-column stores.
If you are unsure when to use NoSQL databases, you might need to consider big data industries’ requirements that are not met by relational databases.
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