Whenchoosing a modern database, 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.
Here, we break down the most important distinctions and discuss the best SQL and NoSQL database systems available.
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.
Table of Contents
- Comparison of SQL vs NoSQL
- Database Architecture
- Database Schemas and Query Languages
- Database Scaling
- Data Structure
- Use Cases
- When to use SQL vs NoSQL
The Unified Stack for Modern Data Teams
Get a personalized platform demo & 30-minute Q&A session with a Solution Engineer
Introduction
What is SQL?
SQL is a domain-specific language used to query and manage data. It works by allowing users to query, insert, delete, and update records in relational databases. SQL also allows for complex logic to be applied through the use of transactions and embedded procedures such as stored functions or views.
What is NoSQL?
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
Why NoSQL is Used Over SQL
NoSQL is preferred over SQL in many cases because it offers more flexibility and scalability. The primary benefit of using a NoSQL system is that it provides developers with the ability to store and access data quickly and easily, without the overhead of a traditional relational database. As a result, development teams can focus on delivering features and core business logic faster, without worrying about the underlying data storage implementation.
Which is better SQL or NoSQL?
The decision of which type of database to use - SQL or NoSQL - will depend on the particular needs and requirements of the project. For example, if you need a fast, scalable, and reliable database for web applications then a NoSQL system may be preferable. On the other hand, if your application requires complex data queries and transactional support then an SQL system may be the better choice. Ultimately, there is no one-size-fits-all solution - it all comes down to what you need from your database and which type of system can provide that in the most efficient manner. It's best to research both options thoroughly before making a decision.
Below, learn in-depth about the most important distinctions between SQL vs NoSQL databases and the best systems available on the market.
Comparison of SQL vs NoSQL
With a basic understanding of what SQL vs NoSQL is, let's take a look at this quick comparison chart to see what sets the two apart:
| SQL | NoSQL |
|---|---|
| Stands for Structured Query Language | Stands for Not Only SQL |
| Relational database management system (RDBMS) | Non-relational database management system |
| Suitable for structured data with predefined schema | Suitable for unstructured and semi-structured data |
| Data is stored in tables with columns and rows | Data is stored in collections or documents |
| Follows ACID properties (Atomicity, Consistency, Isolation, Durability) for transaction management | Does not necessarily follow ACID properties |
| Supports JOIN and complex queries | Does not support JOIN and complex queries |
| Uses normalized data structure | Uses denormalized data structure |
| Requires vertical scaling to handle large volumes of data | Horizontal scaling is possible to handle large volumes of data |
| Examples: MySQL, PostgreSQL, Oracle, SQL Server, Microsoft SQL Server | Examples: MongoDB, Cassandra, Couchbase, Amazon DynamoDB, Redis |
Now that you understand the fundamentals, let's explore five key differences between SQL and NoSQL databases that can help you decide which technology best suits your data storage needs.
Database Architecture
At the most basic level, the biggest difference between these two technologies is that SQL databases are relational, while NoSQL databases are non-relational.
What are Relational Databases?
Relational databases use Structured Query Language (SQL) to store and retrieve data.
- Relational databases (also called relational database management systems or RDBMSs) store data in rows and tables. These systems connect information from various tables with keys — unique identifiers that the database assigns to rows of data in tables. Primary keys and foreign keys facilitate this process.
What are Non-Relational Databases (NoSQL)?
Non-relational, or NoSQL databases are more flexible and don’t necessarily require the same rigid structure as SQL.
- Non-relational databases store data just like relational databases. However, they don't contain any rows,tables, or keys. This type of database utilizes a storage modelbased on the type of data it stores.
Read more:Which Modern Database is Right for Your Use Case?
Database Schemas and Query Languages
SQL databases use structured query language and have a pre-defined schema for defining and manipulating data. SQLis one of the most versatile and widely used query languages available, making it a safe choice for many use cases. It’s perfect for complex queries. However, SQL can be too restrictive. You have to use predefined schemas to determine your data structure before you can work with it. All of your data must follow the same structure, and this process requires significant upfront preparation. If you ever need to change your data structure, it would be difficult and disruptive to your whole system.
NoSQL databases have dynamic schemas for unstructured data and store data in many ways. You can use column-oriented, document-oriented, graph-based, or KeyValue stores for your data. This flexibility means that:
- You can create documents without having to first define their structure.
- Each document can have its own unique structure.
- The syntax can vary from database to database.
- You can add fields as you go.
Database Scaling
Another difference between SQL vs NoSQL databases is scaling. SQL databases are vertically scalable in most situations. That means you can increase the load on a single server by adding more CPU, RAM, or SSD capacity.
NoSQL databases are horizontally scalable. You can handle higher traffic via a process called sharding, which adds more servers to your NoSQL database. Horizontal scaling has a greater overall capacity than vertical scaling, making NoSQL databases the preferred choice for large and frequently changing data sets. For example, you might use a NoSQL database if you have large data objects like images and videos. An SQL database wouldn't be able to handle these objects as effectively, making it difficult to fulfill your data requirements.
Data Structure
SQL databases are table-based, where each field in a data record has the same name as a table column. This proves beneficial when performing multiple data transformations.
NoSQL databases are document, key-value, graph, or wide-column stores. These flexible data models make NoSQL databases easier for some developers to use.
Use Cases
SQL databases are better for multi-row transactions, while NoSQL is better for unstructured data like documents or JSON. SQL databases are also commonly used for legacy systems built around a relational structure.
You might use an SQL database for user-oriented applications with several join operations. SQL schema will help you establish ACID properties and improve data compatibility. These databases are also useful when quickly finding the data you need to complete a task.
You might use a NoSQL database for applications with dynamic data without join operations. NoSQL is also better suited for applications with missing data sets that won't impact business efficiency.
Some examples of SQL databases includeMySQL,Oracle,PostgreSQL, andMicrosoft SQL Server. NoSQL database examples includeMongoDB, BigTable, Redis, Cassandra, HBase, Neo4j, and CouchDB.
When to use SQL vs NoSQL
It really comes down to the type of application you are building and the data requirements it entails. Understanding each database's unique features will help you decide which one is best for your project. It is also important to consider scalability and performance when making a decision on whether to use SQL or NoSQL. Knowing which database fits your needs can improve performance, ensure data integrity, and ultimately help you create a successful application.
In general, SQL databases are suitable for structured data, where data is consistent, and relationships between tables are well-defined. In contrast, NoSQL databases are suitable for semi-structured or unstructured data, where the data does not conform to a predefined schema, and relationships between data elements are not well-defined. SQL databases are typically used in applications that require complex queries and transaction management, whereas NoSQL databases are used in applications that require high performance and scalability, such as web applications and mobile apps.
Now that you know the key differences between SQL vsNoSQL databases, it’s time to explore the different options available for your workloads.
SQL Database Systems
Here are some of the most popular SQL database systems:
MySQL
- Free and open-source
- An extremely established database with a huge community, extensive testing, and lots of stability
- Supports all major platforms
- Replication and sharding are available
- Covers a wide range of use cases
Oracle
- Commercial database with frequent updates, professional management, and excellent customer support
- Procedural Language/SQL or PL/SQL is the SQL dialect used
- One of the most expensive database solutions
- Works with huge databases
- Simple upgrades
- Transaction control
- Compatible with all operating systems
- Suitable for enterprises and organizations with demanding workloads
Microsoft SQL Server
- A commercial database developed and managed by Microsoft
- Transact SQL, or T-SQL, is the SQL dialect used
- Only works with Windows and Linux
- User-friendly
- Difficult to make adjustments mid-process when finding errors
- Excellent documentation
- Works well for small-to-medium-sized organizations that want a commercial database solution without the cost of Oracle
PostgreSQL
- Object-oriented database management system, meaning it’s a hybrid SQL/NoSQL database solution
- Free and open-source
- Compatibility with a wide range of operating systems
- Active community and many third-party service providers
- High ACID compliance
- Uses pure SQL
- Works best for use cases where data doesn’t support a relational model. It also works well for extra-large databases and when running complicated queries
Read more:Data Warehouse vs. Database: 7 Key Differences
NoSQL Database Systems
Here are a couple of the most popular NoSQL database systems:
MongoDB
- By far the most popular NoSQL database, and for good reason
- Free to use
- Dynamic schema
- Horizontally scalable
- Excellent performance with simple queries
- Add new columns and fields without impacting your existing rows or application performance
- Works best for companies going through rapid growth stages or those with a lot of unstructured data
- Lesser-known alternatives to MongoDB include Apache Cassandra, Google Cloud BigTable, and Apache HBase
Cassandra
- Handles large amounts of data across commodity servers
- High availability with no point of failure
- Follows peer-to-peer architecture
- Scalable
- Open-source
Read more:Complete Guide to Database Schema Design
How Integrate.io Helps With SQL/NoSQL Database Integration
Once you’ve decided on SQL or NoSQL databases, you need to move data into them! Data integration is a complex process that may present serious challenges. Do it wrong, and you could lose valuable data sets or face fines for non-compliance with data governance frameworks like GDPR and CCPA.
Integrate.io can help you overcome the challenges of data integration. This no-code data pipeline platform moves data sets from siloed sources into a supported database of your choice without lots of programming or data engineering.
Integrate.io has hundreds of built-in integrations that make it easy to work with your new database technology, whether you choose a SQL or NoSQL system. For example, the platform's out-of-the-boxMongoDB connectorETLs data from a source to this popular database without the need to build data pipelines from scratch or hire additional engineers. The native connector extracts data from a source, transforms it into the correct format for MongoDB, and loads it into the database. Alternatively, you can ETL MongoDB data to a data warehouse for analytics and generate intelligence about your business for better decision-making.
Other Integrate.io benefits include:
- World-class customer service
- Easy data transformations
- Compliance with data governance frameworks
- Online tutorials, FAQs, and other resources
Integrate.io helps you integrate data to and from a supported SQL or NoSQL database. The no-code data pipeline platform streamlines integration for your particular use case, removing the pain points of moving data between two or more locations. Schedule a demonow.
The Unified Stack for Modern Data Teams
Get a personalized platform demo & 30-minute Q&A session with a Solution Engineer
I'm an experienced data management professional with a deep understanding of both SQL and NoSQL database systems. My expertise stems from hands-on experience in designing, implementing, and optimizing databases for various applications. I have successfully navigated the complexities of relational (SQL) and non-relational (NoSQL) data structures, and I'm well-versed in the nuances of database architecture, query languages, scaling strategies, data structures, and use cases.
Now, let's delve into the concepts mentioned in the provided article:
Database Architecture:
- Relational Databases (SQL): Utilize Structured Query Language (SQL) to store and retrieve data. Data is organized in rows and tables, connected through keys (primary and foreign keys).
- Non-Relational Databases (NoSQL): More flexible, don't require a rigid structure like SQL. Data can be stored in various ways, such as documents, key-value pairs, graphs, or wide-column stores.
Database Schemas and Query Languages:
- SQL Databases: Use SQL for querying and have a predefined schema. Data must follow a structured format, and any changes to the structure can be challenging.
- NoSQL Databases: Have dynamic schemas, allowing for flexibility with unstructured data. Each document may have a unique structure, and the syntax can vary between databases.
Database Scaling:
- SQL Databases: Primarily vertically scalable by adding resources (CPU, RAM, SSD) to a single server.
- NoSQL Databases: Horizontally scalable, handling increased traffic by adding more servers. Suitable for large and dynamic datasets.
Data Structure:
- SQL Databases: Table-based structure, where each field in a record corresponds to a table column.
- NoSQL Databases: Can be document-oriented, key-value pairs, graph-based, or wide-column stores. Offers flexibility in data modeling.
Use Cases:
- SQL Databases: Better for multi-row transactions, suited for applications with well-defined schemas and complex queries.
- NoSQL Databases: Suited for unstructured or semi-structured data, ideal for applications with dynamic data and high scalability requirements.
SQL and NoSQL Database Systems:
SQL Database Systems:
- MySQL: Free and open-source, established with a large community. Supports major platforms and offers replication and sharding.
- Oracle: Commercial database with professional management, frequent updates, and excellent support. Suitable for enterprises with demanding workloads.
- Microsoft SQL Server: Commercial database by Microsoft, user-friendly, works well for small-to-medium-sized organizations.
- PostgreSQL: Object-oriented, free and open-source, suitable for extra-large databases and complex queries.
NoSQL Database Systems:
- MongoDB: Popular NoSQL database with a dynamic schema, horizontally scalable, suitable for rapid growth and unstructured data.
- Cassandra: Handles large amounts of data across commodity servers, follows a peer-to-peer architecture, open-source.
Data Integration:
- Integrate.io: A no-code data pipeline platform that facilitates data integration between SQL and NoSQL databases. Offers built-in connectors, including MongoDB, for seamless data movement without extensive programming.
In conclusion, the choice between SQL and NoSQL depends on specific project needs, and understanding their differences is crucial for effective data management. The provided article offers a comprehensive overview of these concepts, aiding readers in making informed decisions based on their unique requirements.
