SQL vs NoSQL Databases: What is the Difference?

When developing an application, one of the most important decisions is selecting an appropriate database. The critical question is: Which database is the best choice? Relational (SQL) or Non-Relational (NoSQL)? So in this blog, we will discuss the key differences between SQL and NoSQL databases that software developers must consider when developing an application.

What is SQL?

SQL (Structured Query Language) is a programming language used to query (extract, store, insert, delete, update) and manage data in a relational database management system (RDBMS). In relational databases, data is stored in rows and tables that are connected in several ways. Here, one table entry can connect to one or many other table entries that are related to many entries in another table.

• Relational databases are designed to handle large amounts of data and complex SQL queries. But a structured schema must be defined before starting with SQL databases. This schema defines the tables, columns, and relationships between them.
• SQL databases can scale vertically i.e. the capacity of the same machine can be increased to handle a larger volume of traffic by adding more CPU, RAM, or SSD capability. Horizontal scaling is not well-supported for SQL databases.
• Several types of SQL databases are available like MySQL, Oracle, PostgreSQL, Microsoft SQL Server, Azure SQL Database, etc. Each has unique features and capabilities. On the other hand, SQL is widely used by top tech companies like Facebook, Google, WhatsApp, etc., for data processing.
• SQL databases have a long history and a huge community base. There are many experts available to support SQL and programming relational data.

What is NoSQL?

NoSQL (Not Only SQL) is a non-relational database that provides a mechanism for the storage and retrieval of large amounts of data in various formats. In other words, NoSQL is not limited to storing data in a tabular format but enables data to be stored in structured, unstructured, and semi-structured forms.

• Unlike SQL databases, NoSQL databases offer more flexibility and allow for dynamic schemas. This makes them a good choice for storing a wide variety of data types.
• NoSQL databases are designed to be horizontally scalable i.e. the system can be easily expanded by adding additional servers or nodes to distribute the load.
• There are several types of NoSQL databases: key-value stores, document-oriented databases, column-oriented databases, and graph databases. Each type is optimized for different data structures and use cases.
• NoSQL databases can handle large volumes of data and support real-time processing and big data analytics. Due to this, companies like Twitter and Google use NoSQL databases.
• Some popular NoSQL databases include MongoDB, Redis, Neo4j, Cassandra, and HBase.
• These days, NoSQL has been adopted quickly and its community support is also growing. NoSQL users do benefit from open-source systems.

ACID Properties in SQL Databases

RDBMS must exhibit ACID properties, which stand for Atomicity, Consistency, Isolation, and Durability. ACID properties guarantee the integrity of data transactions and ensure a high level of reliability for SQL databases. This makes them suitable for use in financial systems and other applications that require reliable transaction processing.

• Atomicity: Transaction is treated as a single, indivisible unit of work. All changes to the database made by a transaction must either succeed or fail as a whole. If any part of the transaction fails, all changes made by the transaction must be rolled back.
• Consistency: Transaction must preserve the consistency of the database. In other words, any changes made by a transaction must comply with the predefined rules and constraints of the database, and must not violate any integrity constraints.
• Isolation: Concurrent transactions do not interfere with each other. In other words, each transaction must appear to be executed in isolation, without any interference from other transactions.
• Durability: Once a transaction is committed, its changes are permanent and cannot be undone, even in the case of system failures. In other words, the changes made by a transaction must be stored permanently in the database, even if the system crashes or loses power.

BASE Properties in NoSQL Databases

NoSQL databases follow either the CAP theorem (Consistency, Availability, Partition tolerance) or BASE (Basically Available, Soft state, Eventually consistent) properties. BASE properties prioritize availability and partition tolerance over immediate consistency, which can be beneficial in distributed systems that require high availability and scalability.

• Basically Available: The system guarantees availability even if it returns outdated data. In other words, the system is designed to always provide a response and remain available even in the event of a failure.
• Soft State: The state of the data can change over time due to the lack of immediate consistency. So the system allows for temporary inconsistencies across nodes during updates or failures.
• Eventually Consistent: The data is replicated to different nodes and eventually reaches a consistent state, but consistency is not guaranteed at a transaction level. Until the system eventually converges to a consistent state, data reads are still possible, even though they may not reflect updated data due to inconsistency.

When to use SQL Databases?

SQL is a good choice when working with highly structured data and handling complex data relationships. Since each piece of information is stored in a single place, we only need to update data once instead of changing multiple files. So, SQL can be used in applications that require strict data integrity and consistency, or transactions that require ACID properties.

SQL databases are flexible and can be easily accessed by any application. For example, when one user updates a specific record, every instance of the database automatically refreshes, and that information is provided in real-time. So, SQL databases are ideal for applications that require real-time updates.

When to use NoSQL Databases?

• NoSQL databases can provide high availability, which is important when dealing with big data that needs to be accessed quickly without interruption.
• NoSQL databases have very flexible schemas. A flexible schema allows us to easily make changes to our database as requirements change. We can iterate quickly and continuously integrate new application features. So, NoSQL is a good choice when we need to scale the system because of changing requirements. It is easier to set up and use than SQL databases.
• NoSQL databases are more suitable for handling complex data models or flexible data needs that cannot be accommodated by a relational model. They can handle unstructured and semi-structured data more easily and can adapt to changes in data structure more quickly. For example, document databases (CouchDB, MongoDB, and Amazon DocumentDB) are suitable for working with large amounts of unstructured data, while Redis is a good choice for quick access to a key-value store. Elastic Search is recommended for flexible searches across a lot of data.
• NoSQL databases are designed to handle large amounts of data spread across many servers. They offer built-in sharding and high availability features that allow for horizontal scaling, which can handle massive amounts of data with no single point of failure.
• NoSQL databases are optimized for fast reads and writes, which makes them suitable for applications that require fast access to data. One more critical point: Queries in NoSQL databases can be faster than in SQL databases. The reason is simple: Data in SQL databases are typically normalized, so some queries require us to join data from multiple tables. As tables grow in size, the joins can become expensive.

Drawbacks of SQL databases

• It can be challenging to scale SQL databases beyond a certain point because their architecture is often limited by the hardware capacity (Vertical Scaling). So SQL databases may not be able to handle an increasing amount of data or traffic without significant upgrades to the hardware.
• SQL databases can be inflexible or not suitable for storing and querying unstructured data. On the other side, SQL databases are based on a strict relational schema, which may not be suitable for all data types. So this inflexibility can limit the types of data that can be stored and queried using SQL databases.
• SQL databases rely heavily on data consistency and integrity, and any errors or issues with the data can have significant consequences for the overall system.
• Operating SQL databases can be expensive, especially if high availability or high performance is required. This can require additional hardware, software, or personnel, which can increase costs significantly.
• SQL databases may have limited performance, especially for big data processing. As data volumes increase, SQL databases can lead to slower response times and decreased performance.

Drawbacks of NoSQL databases

• Unlike relational databases, most NoSQL databases don’t support ACID properties. This is one of the major constraints in building systems with NoSQL when instant data consistency and integrity are required. On the other side, NoSQL databases generally prioritize availability and partition tolerance over consistency. So data consistency can be compromised in certain scenarios. Note: Some NoSQL databases like MongoDB, DynamoDB, and CouchDB can also integrate and follow ACID rules.
• NoSQL databases are optimized for queries and not for reducing data duplication. This can result in larger database sizes, especially if the same data is stored redundantly across multiple nodes. But this trade-off can be acceptable in many cases where query performance and scalability are more important than data size. Additionally, many NoSQL databases do support compression and other optimization techniques to reduce storage requirements.
• Whichever NoSQL database type we select, we may not be able to cover all use cases in a single database. This restricts NoSQL from facilitating a single database for different purposes, and one has to operate with multiple databases and data models. For example, graph databases are excellent for analyzing relationships in data but may not provide what is needed for everyday retrieval of data such as range queries.
• NoSQL databases do not have any standard query language like SQL. In other words, they lack the standard interface of SQL and can be challenging to use for complex queries. Additionally, there is little consistency between NoSQL languages, as they cover a diverse set of technologies. Many NoSQL databases have unique data manipulation languages constrained by specific structures and capabilities.

Combining the best of SQL and NoSQL

In some cases, it may be useful to use both SQL and NoSQL databases in a single application to take advantage of the strengths of each. For example, YouTube stores video content in a NoSQL database and user metadata and other information in a relational database. This allows them to leverage the flexibility and scalability of NoSQL databases for storing large amounts of unstructured data, while also taking advantage of the structured data and complex query capabilities of relational databases.

There are also databases that offer features of both SQL and NoSQL databases. For example, MySQL Document Store combines the structure of an SQL database with the features and flexibility of a NoSQL database. Similarly, MongoDB, a NoSQL database, also offers ACID transactions. This can be useful for applications that need to handle both structured and unstructured data and perform complex queries.

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