SQL vs NoSQL Databases: Fundamental Differences, Use-Cases, and Features

SQL vs NoSQL: How do they differ?

SQL:

• A domain-specific language used to manage data stored in a relational database
• It requires a fixed schema.
• It can be used for data operations such as updating, searching, and deleting database records.
• Examples of SQL databases are Oracle, MySQL, PostgreSQL, MSSQL, etc.

NoSQL:

• A scalable and easy-to-use non-relational database management system.
• It does not require a fixed schema.
• It is used for real-time web pages and big data applications.
• It can store massive amounts of data in distributed data stores.
• Examples of NoSQL databases are MongoDB, HBase, Cassandra, Redis, CouchBase, etc.

The significant differences between these two types of technologies are seen in five characteristics, including

1. Structure:

SQL:

It has a fixed database schema that uses a set of rules that govern the integrity and consistency of data in relational and tabular form. It contains tables with columns and rows, and it has constraints in terms of logical relationships between these data.

NoSQL:

NoSQL has a dynamic database schema that uses column-oriented, document-based, graph databases and key-value pairs. Either one of these four can be easily used alongside NoSQL languages.

2. Language:

SQL:

SQL is a structured query language, as the name implies. Although there are many different dialects in SQL, they all share the same grammar and syntax. The SQL language is versatile and is ideal for complex queries. However, it can be very restricted by its structure. Before you start working with the data, you must understand its structure and relationships.

NoSQL:

The dynamic nature of NoSQL databases allows for the representation of varying structures, as it places less emphasis on preparation and gives freedom in creating new fields with varying syntax and methods across databases. As a group, NoSQL databases do not have the required interface to handle complex queries and hence, are not appropriate for analytical purposes.

3. Scalability:

SQL:

SQL databases are known to be vertically scalable. It adds more power to the underlying cluster or server for CPU, memory, and storage processing. However, SQL can also be horizontally scaled like NoSQL, but this will need sharding or partitioning, which is not well-supported.

NoSQL:

NoSQL databases are horizontally scalable, implying that NoSQL adds additional nodes or servers to share some load. This is hard in relational databases due to the difficulty of sharing related data across different nodes.

4. Community and support:

SQL:

SQL has a more extended history. Thus, it has a stable codebase, massive community, and proven tech standards. SQL online forums support newbies in relational data programming through examples and real-life Q&A sessions.

NoSQL:

NoSQL is relatively new and thus has a smaller community. NoSQL primarily benefits from open systems and community members helping new users.

5. Properties:

SQL:

All SQL databases exhibit the RDBMS (Relational Database Management System) 'ACID' properties.

This implies that no transaction can be half-complete. It can either fail or succeed.

This means that the procedures and steps to validate and prevent system corruption do not and should not change.

In SQL, all transactions that are run simultaneously must not affect each other, and they must result in the same final state as if they were in sequential order.

All transactions' final states must be set in stone. Even when SQL systems fail, they cannot undo the conditions of the transactions.

NoSQL:

NoSQL databases exhibit the BASE properties, i.e.,

This indicates that instead of enforcing consistency across the nodes of a cluster, a NoSQL database system will efficiently allow for the distribution of data across the cluster.

NoSQL lacks immediate consistency and may lead to changes in the data values over time. Due to this, it gives developers the responsibility to enforce data consistency.

NoSQL databases can still provide data reads despite the lack of immediate consistency because they can still do so even though they might not reflect reality.

Advantages: SQL vs NoSQL

SQL:

• Flexible queries
• Solid and well-understood data integrity semantics
• Reduced data storage footprints

NoSQL:

• Flexible data models and high-level data abstractions
• High scalability and availability
• High performance due to limited database functionality range

Disadvantages: SQL vs NoSQL

SQL:

• Rigid data models
• Have single points of failure
• Very limited horizontal scalability

NoSQL:

• No flexibility in access patterns
• No clear interpretation of ACID constraints
• Presence of distributed systems problems

SQL vs NoSQL: When to use them?

There are four factors to consider when deciding which database to use: available technology, flexibility, consistency, and scalability.

1. Scalability:

Choosing which database to use based on scalability is dependent on the efficiency of each option. For instance, consider a scenario of library shelf storage problems. Is it more efficient to create more partitions on the shelves or build new shelves entirely in such a scenario? SQL databases are vertically scalable, which means you can add partitions to the stands to accommodate more books (increase the load). In contrast, NoSQL databases are horizontally scalable, meaning you can build new bookshelves to accommodate more books, that is, by dividing the work over multiple servers.

2. Consistency:

All SQL databases have a consistent design; however, NoSQL databases' design consistency is based on the DBMS. Some NoSQL databases are constant, while others aren’t. Therefore, the type of database to use depends on the kind of data to be handled.

3. Available technology:

One of the essential factors you should consider when choosing a database technology is the current state of development. If you're working with an RDBMS, SQL is a better choice than NoSQL to analyze data behavior, create custom dashboards, and report on the data. It can also provide faster recovery and storage and handle complex queries. If you're working to extend the RDBMS standard structure or build a flexible schema, NoSQL databases are a better choice than SQL. They can also be used for storing and accessing data from various sources, such as distributed databases.

4. Flexibility:

Sometimes, your data has different types and structures. With a NoSQL database, you can design different schemas and store these data structures in the same database. On the other hand, you have to create a more rigid frame with a SQL database.

Conclusion

To summarize, the key differences between SQL and NoSQL can be seen in the following points: SQL is a relational database; it is table-based, vertically scalable, has a predefined schema, and has specialized database hardware for better performance.

Whereas NoSQL is a distributed database, it is document/graph-based, horizontally scalable, uses dynamic schema for unstructured data, and uses commodity hardware. When it comes to choosing which to use, factors that come into play are: flexibility, scalability, consistency, and available technology. These factors, combined with the type of data to be handled, ultimately answer the question of which database to use.