SQL vs NoSQL

There are two primary databases used for storing digital data: SQL (relational databases) and NoSQL (non-relational databases). Though both methods effectively store data, they differ in their structures, scalability, relationships, language, and support.

In this article, you’ll learn about each type of database, how they are similar and different from one another, and how to decide which type of database is suitable for your particular data application.

one of the biggest decisions is picking a relational (SQL) or non-relational (NoSQL) data structure. Both systems offer unique advantages and cater to different needs, making the choice between them crucial for optimal data management.

SQL, or Structured Query Language, is a programming language with a traditional approach that allows relational databases that model predefined schemas to manage structured data like rows and tables. On the other hand, NoSQL, which stands for “Not Only SQL,” offers a more flexible, non-relational approach, ideal for handling unstructured or dynamic data. As businesses evolve and data becomes increasingly diverse, understanding the core differences between SQL and NoSQL is important.

SQL and NoSQL are two database technologies widely adopted by many organizations for different use cases. Both technologies share the common goal of efficiently processing and managing data. Still, there are some significant differences.

This article compares SQL and NoSQL, exploring their key differences in terms of language, structure, scalability, properties and support. We’ll also discuss examples, pros and cons and the most suitable application areas for each database type.

What is SQL?

SQL, short for Structured Query Language, is a widely used database query language developed by IBM in 1970. Originally known as SEQUEL, it became publicly available in 1979. That’s probably why, even today, some people say SQL as “sequel” while the generally accepted pronunciation is “ess-queue-elle” for the 3 letters.

SQL allows users to perform CRUD (Create, Read, Update, and Delete) operations on relational database management systems (RDBMS) based on a structured and tabular format. Users can write SQL queries using keywords and syntax defined by the SQL standard.

Structured Query Language (SQL) is a programming language that allows both technical and non-technically-minded users to query, manipulate, and change data in a relational database.

Organized into columns and rows within a table, SQL databases use a relational model that work best with well-defined structured data, such as names and quantities, in which relations exist between different entities. Within a SQL database, tables are linked through “foreign keys” that form relations between different tables and fields, such as customers and orders or employees and departments.

SQL databases are scalable vertically, meaning that you can increase the maximum load by adding further storage components like RAM or SSD. While in some cases this may mean that SQL databases are limited by the resources available on the server, cloud-based storage and other technologies can provide more scalability with SQL.

The five critical differences between SQL and NoSQL are:

• SQL databases are relational, and NoSQL databases are non-relational.
• SQL databases use structured query language (SQL) and have a predefined schema. NoSQL databases have dynamic schemas for unstructured data.
• SQL databases are vertically scalable, while NoSQL databases are horizontally scalable.
• SQL databases are table-based, while NoSQL databases are document, key-value, graph, or wide-column stores.
• SQL databases are better for multi-row transactions, while NoSQL is better for unstructured data like documents or JSON.

In 1970, the relational model for databases, a model for effectively organizing and storing data in tables in a column and row structure, was outlined by Dr. Edgar Codd in his landmark paper, “A Relational Model of Data for Large Shared Data Banks.” By 1974, IBM developed System R, the first project to implement the relational model for storing data, and they developed SQL as the language to interact with these relational databases.

Over the years, new standards for SQL have been developed, and Oracle, IBM, and Microsoft have each developed relational database management systems (RDBMS) for efficiently, securely, and conveniently using SQL to interact with SQL databases.

Next, open-source languages like MySQL began making SQL and relational databases more accessible to the general population. SQL is now widely accessible both on physical and cloud systems such as AWS, Azure, and Google Cloud.

In SQL databases, the relational model serves as the conceptual foundation, organizing data into structured and interconnected tables. SQL databases are made up of schemas and tables:

• Schema: defines the structure of the database, including tables, their fields, data types, available values, and relationships.
• Tables: The foundational units of a database, representing entities (e.g., information about customers, products, or transactions), with rows containing individual records for each entity (e.g., a specific customer) and columns storing specific attributes about the entity (e.g., the customer’s name or email).

The relational model enforces consistency through two types of keys: (1) primary keys uniquely identifying each record and (2) foreign keys establishing relationships between tables. You can learn more about relational databases in our Introduction to SQL course.

SQL, or Structured Query Language, serves as the interface for managing these databases, enabling the creation, retrieval, updating, and deletion of data through standardized commands. A list of these commands and SQL’s capabilities can be seen in our SQL Basics Cheat Sheet. This model and its components provide a powerful and flexible framework for organizing and querying data in a manner that ensures integrity and coherence across the database.

Advantages of SQL

SQL provides a standard set of commands for defining, querying, updating, and administering data in a relational database management system (RDBMS). Key SQL operations include SELECT (querying data), INSERT (adding new records), UPDATE (modifying existing records), and DELETE (removing records).

When using these commands, SQL ensures that ACID (Atomicity, Consistency, Isolation, and Durability) properties are maintained. This ensures the reliability and consistency of database changes.

1. Atomicity: This property ensures that each attempted set of changes to a database is treated as a single, indivisible unit of work. Either the whole unit is committed to the database, or none of them are if any part of the transaction fails.
2. Consistency: Ensures that a transaction ends with the database in a valid state. The database must satisfy a set of integrity constraints both before and after the transaction.
3. Isolation: Ensures that simultaneous transactions do not lead to inconsistencies in the database. Each transaction appears to execute in isolation, unaware of other transactions running. Isolation prevents interference between transactions and maintains their integrity.
4. Durability: Durability guarantees that once a transaction is committed, its effects persist even in the event of a system failure. The changes made by the transaction are permanently stored in the database, and they survive system crashes or power outages.

Understanding SQL databases

SQL databases, also known as relational databases, structure data into tables with rows and columns. Common SQL databases include MySQL, MariaDB, PostgreSQL, and Oracle. The relational model links data across tables using primary and foreign keys, providing a highly organized, consistent data structure.

Key SQL features and how they impact projects

SQL databases follow a predefined structure, where each table’s columns and data types, like integers, strings, or dates are defined ahead of time. This structure enforces data consistency across records, though it can be limiting when changes are needed later on, as altering the structure may require database migrations or updates that impact application performance temporarily.

SQL databases maintain high data accuracy by following ACID properties (Atomicity, Consistency, Isolation, Durability). These principles guarantee that SQL databases handle data reliably, even under high transaction volumes. Here’s a closer look at what each part means:

SQL databases’ relational model allows developers to use JOIN operations, which pull data from related tables in a single query. This is helpful in applications where data from different tables needs to be combined regularly, such as customer and order data in e-commerce. SQL supports complex queries, aggregations, and nested queries, which makes data management easier.

What is NoSQL?

NoSQL databases are non-relational databases that store data in a manner other than the tabular relations used within SQL databases. While SQL databases are best used for structured data, NoSQL databases are suitable for structured, semi-structured, and unstructured data. As a result, NoSQL databases don’t follow a rigid schema but instead have more flexible structures to accommodate their data-types. Furthermore, instead of using SQL to query the database, NoSQL databases use varying query languages (some don’t even have a query language).

NoSQL databases are scalable horizontally, meaning that they use multiple nodes in a cluster to handle increased workloads. This allows data architects to simply scale them by supplementing clusters with additional servers.

NoSQL non-relational databases work well with unstructured data and typically possess the following properties:

• NoSQL is schema-less (no fixed data model).

• NoSQL databases have a dynamic schema for unstructured data, making integrating data in certain types of applications easier and faster.

• NoSQL uses non-tabular data models, which can be document-oriented, key-value, or graph-based. The most common NoSQL databases include MongoDB, Cassandra, HBase, Redis, Neo4j, and CouchDB.

NoSQL manages the scale and agility challenges you may face in modern applications, especially ones that handle large volumes of rapidly changing data. These demands exist across every industry vertical and application domain, including IoT, user analytics, personalization, ad tech, eCommerce, gaming, and social networks.

NoSQL stands for Not only SQL. It is a type of database that uses non-relational data structures, such as documents, graph databases, and key-value stores to store and retrieve data. NoSQL systems are designed to be more flexible than traditional relational databases and can scale up or down easily to accommodate changes in usage or load. This makes them ideal for use in applications

NoSQL databases

Popular databases that use NoSQL for data processing are:

• MongoDB is a popular document-oriented NoSQL database that supports JSON-based dynamic schemas.
• Redis is a popular open-source, in-memory key-value data store.
• Apache Cassandra is a distributed NoSQL DBMS designed for handling large amounts of data.
• HBase is an open-source column store built on top of Apache Hadoop.
• DynamoDB is a fully managed NoSQL database service provided by Amazon AWS. It has very low latency for read and write operations.

NoSQL (Not Only SQL) databases were created as a response to the challenges posed by the expansion of big data and the increasing need for scalability in database systems. Traditional relational databases struggle to efficiently manage vast amounts of unstructured and semi-structured data commonly seen in modern big data. NoSQL databases represent a more flexible and scalable alternative to relational databases.

They effectively handle diverse data types, accommodate rapid data growth, and distribute data across multiple servers efficiently.

NoSQL solutions like MongoDB, Cassandra, and Couchbase don’t use the same rigid structures that the relational model supports. These systems favor schema flexibility and horizontal scaling, allowing organizations to adapt to the dynamic nature of big data while maintaining performance and reliability. The correlation between NoSQL, big data, and scalability underscores a pivotal evolution in database management, empowering businesses to tackle the challenges presented by the recent exponential growth of data.

Characteristics of NoSQL Databases

Contrary to SQL databases, NoSQL databases use dynamic schemas and support various data models to address diverse application needs.

One defining characteristic of non-relational databases is the use of dynamic schemas. Unlike the predefined, fixed schemas of relational databases, non-relational databases allow for flexibility. Different records in the same database may have different fields. For example, in a customer database, one customer may have a name and age field while the other has a name and email field.

There are various types of NoSQL databases:

1. Document Stores: Utilize a flexible, JSON-like structure to store data as documents. They are useful for handling complex, hierarchical data and support dynamic schemas. They are most commonly used in content management systems, e-commerce platforms, and real-time applications. A popular example of this system is MongoDB.
2. Key-Value Database: This is the simplest form of NoSQL database, where each unit of data is stored as a key-value pair. These systems are efficient and useful in scenarios where lots of data needs to be accessed quickly with fast response times. Two popular examples of key-value NoSQL databases are Redis and Amazon DynamoDB.
3. Graph Database: These databases represent relationships between data entities. Thus, they are most useful in scenarios when relationships are important, such as in social networks or recommendation systems. Two popular examples of Graph databases are Neo4j and Amazon Neptune.
4. Column-Family Database: Column-Family databases represent data in columns instead of rows. They are best for large-scale distributed systems and systems that read and write often such as time-series applications and IoT applications. Commonly used Column-Family databases include Apache Cassandra and HBase.

Advantages of NoSQL

NoSQL databases offer several advantages, with notable strengths in scalability, flexibility, and performance when dealing with unstructured data.

NoSQL databases offer scalability in the form of horizontal scalability and elasticity. They are designed to scale horizontally, allowing organizations to handle increasing amounts of data by adding more servers to a distributed system. This makes them well-suited for applications with growing or unpredictable workloads.

Many NoSQL databases also provide automatic sharding and load balancing, distributing data across multiple nodes to ensure efficient resource utilization and improved performance. This capability enables systems to scale up or down dynamically based on demand and is known as elasticity.

They also provide schema flexibility. NoSQL databases use dynamic schemas, allowing for flexibility in data representation. This means that fields in a record can vary across different documents, accommodating the diverse and evolving data structures commonly encountered in modern applications.

They also perform well on unstructured and semi-structured data types like JSON and XML. Data today is often unpredictable, especially when user-generated, and NoSQL handles storing this data well.

 

Differences Between SQL and NoSQL

At a high level, NoSQL and SQL databases have many similarities.

In addition to supporting data storage and queries, they both also allow one to retrieve, update, and delete stored data. However, under the surface lie some significant differences that affect NoSQL versus SQL performance, scalability, and flexibility.

Language

SQL databases use SQL (Structured Query Language). NoSQL databases use JSON (JavaScript Object Notation), XML, YAML, or binary schema, facilitating unstructured data. SQL has a fixed-defined schema, while NoSQL databases are more flexible.

SQL is a declarative language. It means you have to specify the data you want to retrieve or modify, and the language implementation will handle the rest.

This language is generally easy to understand.  SQL uses the SELECT, INSERT, UPDATE and DELETE keywords to retrieve or modify data in tables while using CREATE and DROP to interact with the database. Additionally, users can:

• Join tables to perform complex operations based on common columns.
• Define relationships between tables.

On the other hand, NoSQL does not have a standardized query language like SQL. Each NoSQL database has its own specific query language, depending on its data model and design. NoSQL leverages data formats like JSON, XML and YML to support unstructured data.

NoSQL is a more flexible language than SQL as it supports dynamic schemas. Similar to SQL, NoSQL provides capabilities for aggregating and processing data.

Data structures

As the name implies, SQL allows performing query operations on relational or tabular data and returns the data in a structured data model consisting of rows and columns. Its strict predefined schema requires users to structure and organize data before performing the query operations. The relationships between tables are established using keys.

In contrast, NoSQL databases do not rely on a specific format and instead offer support for various formats such as:

• Key-value stores store data as a collection of key-value pairs.
• Document stores utilize JSON or XML for storing data.
• Graph databases organize data into nodes and edges.

SQL databases, characterized by structured data models, enforce a predefined schema where data must fit into tables with specific columns and data types. This rigid structure ensures consistency, and it works well for applications with stable and predictable data requirements.

In contrast, NoSQL databases adopt flexible data models, allowing for dynamic and schemaless data storage. This flexibility enables developers to insert data without a predefined schema. NoSQL databases are most useful in scenarios where data structures may be undefined or frequently change.

Key properties

SQL has several properties that distinguish it from NoSQL. First, its primary focus is managing relational database systems:

• The Data Definition Language (DDL) of SQL consists of statements for defining and managing database structures
• The Data Manipulation Language (DML) provides statements for manipulating data within the database.
• Additionally, SQL helps maintain “ACID” properties (Atomicity, Consistency, Isolation, Durability) in RDBMS.

NoSQL also has several properties that differentiate it from SQL. The first key property is its schema-less nature, which offers users greater flexibility in handling data. Unlike SQL, NoSQL does not enforce the use of complex join queries.

Another notable property of NoSQL is its support for distributed architectures.

Database scalability

SQL databases scale vertically, usually on a single server, and require users to increase physical hardware to increase their storage capacities. In effect, while cloud-storage options are available, SQL databases can be prohibitively expensive for businesses when dealing with vast amounts of big data.

NoSQL databases offer horizontal scalability, meaning that more servers simply need to be added to increase their data load. This means that NoSQL databases are better for modern cloud-based infrastructures, which offer distributed resources.

When it comes to scalability, SQL databases are vertically scalable. This vertical scalability is achieved by increasing the hardware capacity, like memory or CPU power. However, vertical scaling can be quite expensive as the volume of data grows.

SQL databases also support horizontal scaling, adding more database instances and distributing them across multiple machines or servers.

NoSQL databases inherently support horizontal scalability. Users can leverage NoSQL databases to handle massive amounts of data. It can be accomplished by adding more servers to the database cluster in a distributed manner and using data partitioning strategies.

Thus, NoSQL databases are typically well-suited for highly scalable applications.

SQL and NoSQL servers emphasize different scaling strengths due to their designs. SQL systems typically rely on vertical scaling, which involves improving and adding resources to the same server to handle increased load. Horizontal scaling, typically seen in NoSQL systems, is achieved by adding more servers or nodes to a distributed system, which then helps increase capacity.

In NoSQL systems, the nodes communicate with each other and distribute the load, so adding more nodes helps increase the overall capacity of the system. This is a more scalable and cost-effective solution for managing a growing database and increased database traffic.

Community support

When considering the community support for each database type, SQL has broader community support due to its well-established community compared to NoSQL. There are many resources to learn SQL, including tutorials, courses, articles and documentation.

Popular SQL databases like MySQL, PostgreSQL and Oracle have large and interactive user bases. They actively engage in discussions and offer valuable support to fellow users.

In contrast, NoSQL is a relatively new technology. Thus, its community is less mature and established than SQL. Yet its community is growing quickly, with specialized communities for different NoSQL databases.

The availability of resources for learning NoSQL is still limited compared to SQL when considering online learning resources. However, the vendor or organization behind a specific NoSQL database often actively supports and engages with the community.

Transactional integrity

SQL and NoSQL database management systems take different approaches to ensuring reliability. SQL relies on ACID properties (Atomicity, Consistency, Isolation, Durability), which, as we discussed above, ensure immediate and strict consistency in the database. SQL queries guarantee that either all or none of the changes made during a transaction are committed to the database and have rules for how to handle concurrent transactions and unexpected events.

On the other hand, NoSQL databases, emphasizing scalability and distributed architectures, adopt the concept of eventual consistency. Eventual consistency acknowledges that, in a distributed system, it may take some time for all nodes to converge to a consistent state after an update. While NoSQL databases sacrifice immediate consistency for scalability and fault tolerance, they ensure that, given enough time, all replicas of the data will eventually converge to the same state.

This trade-off allows NoSQL systems to handle large-scale, distributed environments where real-time consistency might be challenging to achieve efficiently.

Choosing SQL: Pros & cons

Now that we understand the basics and the key differences, let’s look at the benefits and drawbacks of each database type. This will help you get a sense of which might be the right option for your specific use case.

Benefits of SQL

• Well-established. A well-established, well-documented and widely supported query language.
• Various database query operations. Supports a wide range of database query operations, including joining, grouping, filtering and aggregating data, enabling complex data analytics.
• ACID property support. Help enforce the ACID properties of databases and constraints for strict data consistency and data integrity.
• Data consistency. The schema helps promote data consistency.
• Easy to use. Easier syntax and structure. Thus, SQL is easy to learn and use for querying and manipulating data.
• Simple. Useful for simple aggregations and ETL jobs.

SQL drawbacks

• Less flexible. Schema is less flexible as it supports only structured data. That means using complex data structures can be challenging in traditional SQL databases.
• Fixed schema. SQL databases feature a fixed schema. Thus, the structure of tables and relationships must be defined beforehand — and it will be difficult to alter it later.
• Query degradation. SQL query performance can degrade as the volume of data grows. That means you may have to use query optimization techniques to improve its performance.
• SQL complexity. Although SQL has an easy syntax, some database operations may involve complex queries that require significant expertise and experience.
• Debugging complexity. Debugging for errors may be difficult or time-consuming in SQL as it lacks informative error messages.
• Limited scalability. The scalability of SQL is somewhat limited compared to NoSQL.

 

SQL vs NoSQL in Industry

SQL Database Uses

A few examples of industries that rely on SQL databases are:

1. Finance: Many financial institutions manage transactional data and customer records. SQL’s ACID properties ensure the data are accurate and that the transactions lead to an immediately consistent database once processed.
2. Retail: Many retail businesses leverage SQL databases, as they must manage complex relationships related to products, shipping, sales, customers, and supplier information. Their data are also typically well-structured and predictable.
3. Government and Public Sector: Government agencies manage a lot of citizen records and public services which are subject to regulatory requirements. SQL’s structured nature helps with regulatory adherence.

NoSQL Database Uses

A few examples of industries that rely on NoSQL databases are:

1. Social Media: Social media platforms handle large volumes of unstructured data, such as user profiles, posts, and interactions. The flexibility of NoSQL accommodates the dynamic nature of social media content and data.
2. Logistics and Supply Chain: They use NoSQL databases for real-time tracking of shipments, inventory management, and other diverse and dynamic data sources across the supply chain. NoSQL’s performance and scalability for real-time data make it well-suited for this industry.
3. Gaming: The gaming industry leverages NoSQL databases for managing player data, leaderboards, and in-game analytics. The ability to scale horizontally is essential for handling the massive amounts of data generated by online multiplayer games.