Most data engineers, analysts, and IT professionals are familiar with SQL (Structured Query Language) and relational database management systems (RDBMS).
While this type of database has been a standard for decades, organizations need solutions that can handle unstructured data, varying data types, and modern use cases.
To support diverse use cases, data teams have turned to NoSQL databases, which use flexible schemas and have high availability.
In this article, we explain the key features of each database technology and compare SQL vs NoSQL databases in detail. We have also outlined the best scenarios to use each database type so you can make an informed decision about the right system.
SQL is used to create, modify, and query relational databases that drive applications used in e-commerce platforms, financial management systems, healthcare solutions, and more.
Relational databases organize data in tables. Data engineers can define relationships between tables using primary and foreign keys. They can create views, indexes, and triggers for additional functionality.
SQL statements enable database operations, including creating tables, inserting, updating, and retrieving data.
Relational databases can handle complex queries needed for data analytics and business intelligence. Using structured query language, users specify exactly what data they need and how it should be retrieved. This improves query performance and reduces the amount of data transferred over a network.
Some popular SQL or relational database management systems are MySQL, Oracle, Microsoft SQL Server, and PostgreSQL.
SQL databases have three key characteristics:
- ACID properties: SQL databases are ACID compliant. ACID transactions in a database ensure data consistency, reliability, and durability even during hardware or software failures.
- Schema-based data organization: The SQL database schema organizes data in relational databases using tables.
- SQL as a query language: These databases use SQL as the standard query language. This language is over 40 years old and is reliable. All SQL dialects share a common syntax and almost-identical grammar.
Popular SQL databases
There are four well-known SQL databases:
- MySQL: MySQL is an open-source RDBMS released in 1995. It is widely used for web applications. It is fast, scalable, easy to use, and supported by a large community of developers.
- PostgreSQL: PostgreSQL is a robust RDBMS that can handle complex workloads. It is flexible, scalable, and supports advanced features like triggers and stored procedures.
- Microsoft SQL Server: Microsoft SQL Server is an RDBMS that offers features to support data integration, data analysis (OLAP), OLTP, and reporting use cases.
- Oracle Database: Oracle Database SQL, also known as Oracle RDBMS, is used by large enterprises for transaction processing, in-memory data processing, and other use cases.
NoSQL databases are non-relational databases used to store and manage unstructured and semi-structured data, such as social media posts, sensor data, and log files. They provide the added flexibility and scalability needed to match the use cases of modern data teams.
These databases use flexible schemas for data storage. They support varying data models, such as key-value, document, column-family, and graph. This gives data engineers the freedom to design their schema and store different data structures within the same database.
Non-relational databases can support read-heavy and write-heavy workloads using distributed architectures and optimized data models.
They can be faster than relational databases and are used for modern web applications and big data processing.
However, NoSQL languages lack the standard interface which SQL provides, so complex queries can be difficult to execute.
NoSQL databases have three key characteristics:
- Schema-less data organization: NoSQL databases use a dynamic schema that enables data engineers to quickly make database changes to match evolving business needs.
- Data model flexibility: NoSQL databases have flexible data models, such as key-value, document, column-family, and graph, to cater to the specific requirements of a project.
- Scalability: NoSQL databases use a distributed architecture to manage large data volumes. You can scale them horizontally by adding more commodity servers, clusters, or nodes. This is more affordable than SQL databases that often only support vertical scaling, where growing businesses have to migrate to a larger server.
Types of NoSQL databases
There are four types of NoSQL databases:
- Document Databases: Document databases store data in a semi-structured format, typically using JSON or XML documents. Examples of document-oriented databases include MongoDB and Couchbase.
- Key-Value Databases: These databases store data using key-value pairs. A key is a constant that defines the data set, and the value is a variable within the data set. These databases are ideal for the high-speed retrieval of small amounts of data. Examples of key-value databases include Redis and Riak.
- Column-Family Databases: In these databases, data is stored in columns instead of rows, allowing for fast retrieval of large amounts of data. Examples of column-family databases include Apache Cassandra and HBase.
- Graph Databases: These databases use graph data structures to represent the relationships between different data sets. They are used in social networks and recommendation engines. Examples of graph databases include Neo4j and OrientDB.
SQL vs NoSQL Databases
Before we dive into an in-depth comparison of NoSQL and SQL, here’s a table to show their critical differences at a quick glance:
Let’s take a detailed look at the five key differences between NoSQL and SQL databases:
Data Modeling and Structure
In SQL or relational databases, structured data is organized into tables using a fixed schema. Tables consist of rows (records) and columns (attributes). Each column has a defined data type.
The predefined schema defines the structure of the data. Keys are used to determine the relationships between data sets in different tables. SQL databases are suited for hierarchical data storage.
Data is typically normalized to avoid duplication within SQL databases, enforce data integrity, and maintain data consistency.
NoSQL databases use flexible data models to organize data. Standard data model options include key-value, document, column-family, and graph.
They can handle semi-structured and unstructured data required for big data workloads and real-time data streams.
Query Language and Operations
SQL databases use a standardized and powerful query language (SQL) for managing data. When querying relational databases, fluency in one language translates to proficiency in most others. This enables better portability, interoperability, and integration with different database systems.
SQL provides a rich set of operations for data management, including SELECT, INSERT, UPDATE, and DELETE for manipulating data.
SQL databases also have advanced features, like subqueries, joins, and aggregate functions, to enable complex queries and analysis.
The query language and operations in NoSQL databases vary based on the type of database and the data model.
For example, key-value stores use a simple query language limited to basic CRUD (Create, Read, Update, Delete) operations. While other databases, such as a document-oriented non-relational database, have querying capabilities.
SQL databases support vertical scaling. So, when you exceed the capacity of a server, you must invest in additional resources, like memory or processing power, or new servers.
This approach can be expensive and may lead to performance issues if the server is overloaded.
Some SQL databases can also be scaled horizontally, but it can be challenging due to their single-server architecture. Developers and data engineers can use sharding, partitioning, replication, and clustering to overcome scaling issues.
NoSQL databases support horizontal scaling. They distribute data across multiple servers, so organizations can add additional servers or nodes when capacity is exceeded.
Horizontal scaling is especially important for big data applications since it enables greater processing power and more efficient handling of large data sets. Horizontal scaling is also more affordable.
In addition, NoSQL database management systems can automatically balance the workload across servers. This allows them to handle high data volumes and traffic levels better than SQL databases.
Consistency and Transactions
SQL database transactions adhere to the ACID (Atomicity, Consistency, Isolation, Durability) properties to maintain data validity and integrity. This is essential when working with sensitive information.
ACID transactions ensure that changes to the data are made in a consistent and reliable manner. Transactions will either be completed successfully or rolled back if an error occurs.
SQL databases enforce data consistency by using foreign keys and other constraints. However, implementing keys and data constraints can limit scalability and performance.
Some SQL databases offer additional features or optimizations, so there is no trade-off between consistency and performance.
Data consistency and transaction support in NoSQL databases vary from one database to the next.
Some NoSQL databases, such as document stores or graph databases, provide eventual consistency, while others, like key-value stores, may not support transactions at all.
So, NoSQL systems are suitable for applications where immediate consistency is not critical, and the trade-off for improved scalability is acceptable. However, it’s worth noting that every NoSQL database has its own features. MongoDB, for example, supports ACID transactions.
Ecosystem and Community
Relational databases have been around for decades. They have a mature ecosystem and a large community of users, developers, and vendors.
This ecosystem includes various tools, frameworks, and libraries for working with SQL databases, along with books, tutorials, and online resources.
Many vendors offer commercial SQL database products and open-source SQL databases, such as MySQL, PostgreSQL, and SQLite.
Non-relational databases are a relatively recent development. They have a less mature ecosystem and a smaller community. However, the popularity of NoSQL databases has been growing rapidly.
There are many established NoSQL databases, such as MongoDB, Cassandra, and Couchbase, with large and active communities of users and developers.
This ecosystem includes diverse frameworks and libraries for working with NoSQL databases. There are also online resources, conferences, and user groups to provide support.
Use Cases and Examples
When to choose SQL databases
SQL databases are ideal for:
1. Applications requiring complex transactions
If your application requires the execution of complex transactions and queries, SQL is a good choice. Most SQL databases offer powerful query capabilities, like joins and aggregations across multiple tables, to analyze data.
2. Strict consistency and data integrity requirements
If data consistency and integrity are top priorities, an SQL database can ensure strong consistency through ACID transactions. This is essential for applications where data accuracy and security are vital, such as financial or healthcare systems.
3. Mature ecosystem and standardized query language
If you want a mature ecosystem and a large community to support your database operations, SQL databases are the way to go. There is a wealth of documentation and resources for working with SQL systems.
SQL is also the best choice when you want to use a standardized query language for data management across different database systems and third-party tools.
When to choose NoSQL databases
NoSQL databases are ideal for:
1. Applications requiring high scalability and flexibility
If data teams work with large volumes of unstructured or semi-structured data, then a NoSQL database is the best option.
NoSQL databases drive applications that need high scalability and flexibility. These database systems can handle large amounts of traffic and data by distributing data across multiple servers.
2. Projects with diverse data models and structures
NoSQL is a good choice for projects that involve diverse data structures or data models that don’t fit into a relational model.
Advanced NoSQL databases can also provide powerful query and indexing capabilities for efficient data retrieval, even in dynamic data models.
This allows developers to focus on application logic rather than database design, enabling rapid development and faster time-to-market.
3. Use cases where eventual consistency is acceptable
NoSQL databases are used in modern applications where data must be available at all times but at the cost of eventual consistency.
Eventual consistency is a property of NoSQL databases, where updates are distributed asynchronously to maintain high availability. But, there may be a delay before all nodes have the same data.
So, while there is strong consistency over time, data duplication and errors might be a problem in the short term.
Hybrid Approaches: Combining SQL and NoSQL
Hybrid approaches combining SQL and NoSQL technologies are becoming increasingly popular in modern data architectures. These approaches leverage the strengths of both databases to build a flexible and scalable data management platform.
One common approach is to use a NoSQL database as a caching layer for SQL data. This reduces the number of SQL queries to be executed while still ensuring data consistency and durability.
Another approach is to combine the two databases for various data types. For example, a SQL database is used to store structured data, while a NoSQL database is used to store unstructured data.
In addition, many modern databases, such as Apache Cassandra and Amazon DynamoDB, provide hybrid SQL/NoSQL interfaces that allow developers to use SQL queries to retrieve data from a NoSQL database.
This can simplify development by providing a familiar interface while leveraging NoSQL databases’ high-performance levels.
As data architectures become more complex and diverse, hybrid approaches will become increasingly important for organizations that must manage and analyze large data volumes from multiple sources.
Benefits of using both SQL and NoSQL databases
Using both SQL and NoSQL databases in a hybrid approach can provide four major benefits:
- Improved performance: By using NoSQL databases as a caching layer for SQL data, organizations can reduce the number of SQL queries that need to be executed and boost performance.
- Added flexibility: Hybrid approaches allow you to choose the best database to store different data types.
- Improved scalability: By leveraging NoSQL databases in a hybrid approach, organizations can achieve the scalability they need for modern data architectures at lower costs.
Examples of hybrid databases
There are three commonly used hybrid database solutions:
1. Microsoft Azure Cosmos DB: Azure Cosmos DB is a fully-managed NoSQL and relational database designed for mission-critical applications. It offers multiple data models, APIs, and enterprise-grade security.
2. Google Cloud Spanner: Google Cloud Spanner is a distributed, horizontally scalable relational database service. It is a fully managed database solution that supports SQL queries and is highly available, consistent, and scalable.
3. FaunaDB: FaunaDB is a serverless, cloud-native NoSQL database designed for modern applications that require flexible and secure data storage. It provides a range of features to support developers in building real-time applications.
SQL and NoSQL databases differ in their querying language, data models, and schemas.
An SQL or relational database can support complex queries needed for data analysis and business intelligence. NoSQL databases are built for fast performance, high data availability, and scalability. They can be designed to support varying applications.
Understanding the pros and cons of each database helps data engineers build data management solutions that help achieve overall business goals.
The type of database solution and approach you implement must cater to the specific use cases and data needs of your organization.
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