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1970s Edgar F. Code: proposed the relational database model commonly known as SQL databases. These databases are primarily designed for processing structured data with relational models. They can handle transactional data, which involves storing and manipulating data in tables with predefined schemas. Popular examples of SQL databases are MySQL, PostgreSQL, and Oracle Server.
They gained enormous popularity in the 1980s, but since then the demand and volume of data has increased, and the need for different types of data has become more diverse to the point that SQL databases have become difficult. In addition, they are difficult to scale horizontally, which makes them unsuitable for processing large amounts of data.
To address these limitations of SQL databases, NoSQL databases came into the picture in the early 2000s. They are document-based databases and use fast key-value pairs to store data. They can parse data from documents and store that data under keys, rather than defining strict tables of information, unlike SQL databases.
NoSQL databases have now become mainstream and offer various advantages over SQL databases. But that doesn’t necessarily mean that NoSQL databases are better than SQL. Both SQL and NoSQL databases serve different purposes and use different approaches to data management. One is used for relational data and the other for non-relational data. SQL databases are still used where there is a need to run complex queries and the database schema is well defined. Famous examples are MongoDB, Cassandra, Neo4J and Redis.
But NoSQL databases are better than SQL databases in certain areas discussed below.
Below are some key features where NoSQL databases perform better than SQL databases.
- Flexibility.
This means that data can be dynamically added or removed without changing the original database structure. This means that unlike SQL databases, they do not require hard schemas. They are designed to handle any structured, semi-structured or unstructured data format. This gives developers the freedom to focus on developing the application rather than worrying about the database schema.
- Scale.
NoSQL databases support horizontal scaling, which means we can scale it by adding more servers instead of increasing the capacity of a single server. This makes processing a large number of queries more powerful.
- High accessibility.
With its database replication features across multiple servers, it provides very low latency and almost zero downtime to its users. In addition, they split traffic among themselves to reduce the load on a single server.
- Performance.
They are designed to optimize read and write performance, making them suitable for real-time data processing applications where terabytes of data are generated daily. They have faster query response times and support database sharing, improving their overall performance.
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These are some points where NoSQL databases perform better than SQL databases. The next section will discuss the types of NoSQL databases and their use cases.
There are different types of NoSQL databases, each with their own advantages and limitations. Below we have discussed some popular ones.
Key-value
It is the most flexible NoSQL database. It requires a key-value pair to store data. A key can be a unique attribute, like an id, that matches a particular value. The application has a control for storing any type of data in the value field. They are suitable for real-time applications, caching or session management. Redis and Riak are popular examples of key-value databases.
The use case.
This database is best suited for e-commerce platforms where large amounts of customer-related data such as orders, user profiles, and product catalogs are generated daily. Due to its low latency and fast processing, it is suitable for real-time inventory management and high traffic management.
Document orientation
These databases store data in documents, mostly in the form of JSON objects. This is most suitable for storing semi-structured or unstructured data where the document fields do not need to be clearly specified. MongoDB is a popular example of a document-oriented database.
The use case.
This is best suited for Content Management Systems, such as a blog site. Data in the form of articles, comments, categories and tags can be stored and quickly retrieved. Document-based data is suitable for storing unstructured data such as text, images, links, etc. Additionally, the flexible behavior of the schema allows for easy changes to the data model.
Graphic databases
This type of NoSQL database is best suited for data that is strongly correlated with each other. They store data in the form of nodes and edges and are used to represent complex relationships between objects. It is best suited for building social media apps and recommendation engines. Well-known examples are Neo4J and InfoGrid.
The use case.
They are best suited for building recommendation engines. Take the example of Youtube, which recommends videos to users based on their viewing history. Graph databases can store and process correlated data and quickly deliver relevant content.
Column-oriented databases
In this type of NoSQL database, data is stored in column families. Similar data types are grouped into cells and stored in columns of data instead of rows. A column can even contain multiple rows and columns within itself that have their own unique ID.
In a traditional SQL database, the query is executed row by row. But in a columnar database, the query is executed only for the specific column we want. This saves time and makes it suitable for processing large data sets. They can be similar to an SQL database in that they include some form of schema management and work much like a relational table. But they are much more flexible and efficient than SQL databases, making them in the category of NoSQL databases.
Popular examples of columnar databases are Apache HBase and Apache Cassandra. For more information on this topic, you can read this article by Alex Williams.
The use case.
Column-family databases are best suited for data warehousing applications. These applications require large amounts of data analysis for business intelligence with high write throughput, and column-family databases take full responsibility for this.
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In this article, we learned what NoSQL databases are and a look at the fundamental difference between SQL and NoSQL databases. We then discussed popular types of NoSQL databases and their use cases.
There is always a What to choose? Between SQL and NoSQL databases. To make the right choice, you must first understand your application’s use case, data model, scalability, and performance, and then decide.
I hope you enjoyed reading the article. If you have any comments or suggestions, please contact me on Linkedin.
Aryan Garg B. Tech. Electrical Engineering student, currently in final year of B.Sc. His interest lies in Web Development and Machine Learning. He pursued this interest and wants to work more in these directions.