Relational database management systems (RDBMs) are ideal for complex, interrelated, structured data. SQL databases store data in tables and rely on well-known structured query language (SQL).
NoSQL systems easily manage large amounts of data, and data structure is not important for their use. A NoSQL database vs SQL databases offers flexibility in data models and is schema-less.
A NoSQL database structure is flexible enough to allow a wide range of data storage formats, including:
- Key-value pairs
- Columnar databases
- Graph databases
- Document-oriented
- NoSQL databases
One key difference between SQL and NoSQL databases is that SQL databases offer ACID guarantees for data (atomicity, consistency, isolation, durability). Thus, relational databases are typically used for applications with well-defined transactions, such as banking and traditional businesses.
The NoSQL vs relational type of database sacrifices some of the ACID properties in favor of better scalability and distributed processing. NoSQL is often the first choice for real-time analytics, content management systems, IoT applications, and scenarios where flexible performance is critical.
Here are some general tips for when to use NoSQL vs SQL:
Use SQL vs NoSQL databases when:
- The data has a well-defined structure with complex relationships;
- There is a need for transactions with ACID properties;
- Strong consistency and data integrity are required;
- The application has relatively low scalability requirements.
Use NoSQL vs SQL database management systems when:
- The type of data is unstructured or semi-structured, and its schema may evolve over time;
- There is a need to scale horizontally, and handle massive amounts of data and high throughput;
- Flexibility in data modeling is more important than strict consistency;
- A goal is building real-time applications that demand low-latency access to data;
- A goal is storing and processing diverse data types efficiently.
NoSQL vs SQL Performance Comparison
In terms of NoSQL vs SQL performance, scaling, and other benchmarks, these databases have different characteristics and trade-offs. Ultimately their performance and advantages will depend on the user’s workload and goals.
Relational databases typically excel in handling complex SQL queries involving joins and aggregations—but this is especially true when the data volume is not excessively large. They optimize for transactional consistency and ACID properties, which can sometimes impact performance under high load.
In terms of NoSQL vs SQL speed, the former are generally designed for high-performance, low-latency access to data. They prioritize horizontal scalability and can handle massive volumes of read and write operations distributed across multiple nodes. However, the performance may vary based on the specific data model and workload.
See this post for more about NoSQL vs SQL performance benchmarks.
SQL vs NoSQL Advantages and Disadvantages: What to Consider
When you consider why use NoSQL vs SQL databases, there are a few standout advantages and disadvantages of each type to be aware of:
Advantages of SQL vs NoSQL databases
- Data integrity. Relational databases enforce ACID guarantees, ensuring data integrity and reliability, especially in transactional systems.
- Mature technology. Relational databases offer robust features, mature tooling, and widespread support in the industry.
Powerful query language. SQL offers a standardized means for manipulating data, and enabling complex queries, joins, and aggregations. - Schema enforcement. Relational databases rigidly maintain data consistency and facilitate data validation.
- Comprehensive indexing. Relational databases support various indexing techniques, allowing efficient retrieval of data based on different criteria.
Disadvantages of relational databases
- Scalability limitations. Relational databases may be challenged by massive volumes of data or high-throughput workloads, particularly in distributed environments.
- Schema rigidity. Modifying the database schema can be complex and may require downtime or schema migrations, making it less suitable for agile development and evolving data requirements.
- Performance overhead. The overhead of maintaining ACID properties and enforcing relational constraints can impact performance, especially in write-heavy applications.
- Vertical scaling. Relational databases typically require adding more resources to a single server to scale, which can be costly and have practical limits.
How NoSQL vs SQL Advantages Impact the Choice
What are the advantages of NoSQL databases over RDBMS? Most of this is dictated by the user’s specific goals and workload, but there are a few general benefits of NoSQL vs SQL:
Flexible data model in NoSQL vs SQL: when to use
This is among the key NoSQL vs SQL differences, because relational databases enforce a rigid schema that requires upfront design. Any changes to it can be cumbersome and may require downtime or schema migrations.
Greater flexibility in data modeling, and data querying and storage in various formats without predefined schemas are among the main advantages of using NoSQL databases. This flexibility makes them suitable for agile development, denormalization, and applications with evolving data requirements.
Consistency and availability in relational database vs NoSQL database
Relational databases vs NoSQL databases prioritize strong consistency, but maintaining it across distributed systems can be challenging and may introduce latency. The trade-off in favor of eventual consistency is among the advantages of NoSQL, because it makes this kind of database better suited for distributed and fault-tolerant architectures.
SQL vs NoSQL scalability
Relational databases traditionally scale vertically, meaning that improving performance requires upgrading hardware resources like CPU, RAM, and storage on a single server, a cost-limited approach.
Horizontal scaling is another of the advantages of NoSQL vs SQL, because it allows users to add more servers or nodes to distribute the workload and accommodate growing data volumes. This allows for better scalability and fault tolerance, although it may require more complex distributed systems management.
Disadvantages of NoSQL databases
Lack of ACID transactions
Many NoSQL databases sacrifice strong consistency and ACID guarantees for better scalability and performance, which may not be suitable for all use cases.
Limited query capabilities
Some NoSQL databases offer querying capabilities that are relatively limited compared to SQL-based relational databases. This can make complex data analysis and reporting challenging.
Learning curve
NoSQL databases often require users to learn new concepts and programming languages.
Eventual consistency
NoSQL databases often rely on eventual consistency models, which can cause inconsistencies in data in distributed systems. This requires careful application design, strong developer chops, and may take extra time in the error handling process.
SQL vs NoSQL security
In contrast to SQL databases, NoSQL databases tend to be less secure. Because they are less structured to allow faster access to data, they usually have fewer native security features and lack confidentiality and integrity attributes. Users also can’t segregate permissions in the same ways because NoSQL databases lack a fixed and well-defined schema by design.
How is Data Structured in a Relational Database vs NoSQL Database?
Relational databases structure data in tables. Each table is organized into rows and columns defined by a schema, which specifies the data types stored in the table and any constraints or relationships with other tables. Primary and foreign keys also establish relationships between tables and maintain data integrity.
There are several kinds of NoSQL databases, each with its own data model and structure:
Column-family stores such as ScyllaDB organize data into columns grouped into families. Each row can have a different number of columns, and columns are stored to enable efficient read and write operations for sparse data sets. Column-family stores are well-suited for analytical workloads and time-series data.
Document-oriented NoSQL databases such as MongoDB collect data in documents, with each capable of a different structure, and fields within documents capable of variance. Documents may be organized into collections or buckets.
In offerings such as Redis, data is stored in key-value pairs, and may be any type, such as strings, numbers, or complex objects. Each key is unique and maps to a corresponding value. Key-value stores offer high performance for simple read and write operations but may lack advanced querying capabilities.
Graph databases such as Neo4j represent data as a network of nodes and their relationships. Queries typically involve complex relationship analysis of connections between nodes.
NoSQL vs SQL Examples and Use Cases
Here are some common SQL vs NoSQL use cases:
NoSQL vs SQL for ecommerce. In terms of transactional systems, SQL databases are more common in this space. This is because of the need for applications where ACID transactions are critical, such as banking systems, e-commerce platforms that store customer information, and inventory management systems.
Business applications. Complex data relationships, reporting, and ad-hoc queries, such as customer relationship management, enterprise resource planning, and HR systems.
Data warehousing. Aggregating, transforming, and analyzing structured data from multiple sources for business intelligence and decision-making.
Regulatory compliance. Strict regulatory requirements for data integrity, audit trails, and access controls, such as in healthcare, finance, and government.
NoSQL database use cases include:
NoSQL vs SQL for analytics. For big data SQL vs NoSQL the latter is better-suited, and is optimized for big data analytics and real-time processing applications that require high throughput, such as IoT, sensor data processing, and log analytics. Certainly there are techniques for speeding queries in SQL systems, but there is no question whether it is more advantageous to choose NoSQL or SQL for big data.
Content management and personalization. NoSQL databases are ideal for social media platforms, recommendation engines that require flexible data models, and other systems that demand rapid development and scalability to handle dynamic, personalized content.
High-volume web applications. NoSQL databases are commonly used for social networks and gaming platforms that demand horizontal scalability, low-latency access to data, and sufficient fault tolerance for multiple concurrent users and unpredictable workloads.
Agile teams. NoSQL databases are better for rapidly evolving data, where developers need the flexibility to iterate quickly without rigid constraints.
Geospatial and graph applications. NoSQL databases, particularly graph databases, are used in applications that involve complex relationships and require efficient traversal of graph structures.
Combination of SQL and NoSQL Databases
Using SQL and NoSQL together allows for the strengths of both to address different aspects of data management:
Adopt a polyglot persistence approach. For example, use a relational database for complex queries and a NoSQL database for high-volume, flexible data models.
Replicate data between types of databases. This maintains consistency and enables different types of queries. Implement data replication mechanisms or custom synchronization scripts to keep data current between databases.
Design applications as loosely coupled microservices. Some microservices can use relational databases for transactional data management, while others might use NoSQL systems for specialized processing or caching.
Implement the Command Query Responsibility Segregation pattern. This separates the read and write operations into different data stores. Use a relational database for handling write operations (commands) and a NoSQL database optimized for read-heavy workloads for query operations (queries).
Perform hybrid queries or aggregations. Combine data from relational and NoSQL databases at the application layer. Fetch data from multiple databases and perform joins or transformations in the application code to fulfill complex query requirements.
Adopt a data lake architecture. A NoSQL database may be ideal for storing structured and unstructured data centrally, while a relational database may be best for handling data warehousing and analytics.
Use event sourcing. Capture and store all changes to the application’s state as a series of immutable events in a NoSQL database optimized for high write throughput and use a relational database for materialized views or snapshots to support query operations.
Use polyglot object-relational mappers. These support multiple database backends, allowing unified interaction with both relational and NoSQL databases through a single interface. This approach simplifies development and maintenance by abstracting away the differences between database types.
