The deep and sustained rise of NoSQL cloud databases, such as Amazon DynamoDB, MongoDB, and Apache Cassandra, marks a significant shift in the way organizations manage vast and varied data sets. There is nothing wrong with traditional relational database management system. In fact, many NoSQL databases have added support for SQL-style queries.
But in a world where businesses are inundated with unstructured data from mobile devices, social media, the cloud, sensors, and other sources, NoSQL is simply better. And compared to an RDBMS, it is better for managing massive amounts of unstructured data, horizontal flexibility, and schema flexibility.
That said, NoSQL databases are more flexible when it comes to data organization and easier to use when it comes to unstructured data. Therefore, choosing between relational and NoSQL cloud databases will depend on your needs in terms of schema structure (data type), data organization, scaling needs, and other factors that we examine below.
Relational Database vs NoSQL Database: Comparison Table
| Database type | Scheme structure | Scale approach | Data organization | Transaction properties | Easy to use (Unstructured data) |
|---|---|---|---|---|---|
| Relational | Predefined | Vertical | Structured | ACID Compliant | Moderate |
| Non-SQL | No scheme | Horizontal | Flexible | BASE Compliant | Easy |
NoSQL database
A NoSQL database is designed to offer high operational speed and flexibility in the types of data stored and its structure. They are mainly used for large distributed data sets and are particularly effective when dealing with big data and real-time applications.
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Unlike relational databases, NoSQL databases are specifically designed to handle rapidly changing unstructured data, making them ideal for organizations that handle dynamic and varied data formats.
Advantages
- Scalability: The ability of NoSQL databases to scale horizontally makes them ideal for transferring large amounts of data.
- Data modeling flexibility: Because they are schema-less, NoSQL databases are ideal for various types of data formats, such as document stores, key-value stores, graph databases, and more.
- High availability: Designed for distributed environments, NoSQL databases offer robust solutions for maintaining high availability, which is critical for continuous operations.
- Easy to use: In terms of managing unstructured data that does not fall within the rigid structure of relational databases, NoSQL databases are easier to use.
- Performance with unstructured data: NoSQL databases are very adept at managing unstructured and semi-structured data, offering high performance in different scenarios.
- Profitable at scale: NoSQL databases tend to be more cost-effective, especially in cloud environments, compared to traditional relational databases.
Cons
- Learning curve due to lack of standardization: Unlike relational databases that use unified SQL, NoSQL databases have distinct and unique query languages that database professionals must learn and understand.
- Complexity in data consistency: Due to the distributed nature and eventual consistency model of NoSQL, achieving data consistency is often more complex.
- Limited transactional support: NoSQL databases often do not provide full support for transactions in atomicity, consistency, isolation, and durability, which can be a limitation for some applications.
- Challenges with backup and recovery: The distributed architecture of NoSQL databases can complicate backup and recovery processes, requiring more sophisticated strategies compared to RDBMSs.
Relational database
Relational databases have been around for much longer. Unlike NoSQL databases, they store and provide access to related data points. RDBMS are based on a model that uses a structure of tables linked by defined relationships that express dependencies between data.
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Primarily, relational databases are used for data storage and retrieval operations in applications where data accuracy, consistency, and integrity are paramount. They are the backbone of a wide range of business applications.
Advantages
- Strong consistency: Relational databases are known for their strong consistency models. They are reliable and have predictable data transactions, a critical requirement for many business applications.
- Structured data integrity: Relational databases stand out for maintaining the integrity of structured data, with a well-defined schema that enforces data types and relationships.
- Mature and standardized: RDBMS technologies are mature and have established standards, particularly SQL.
- Strong transactional support: Relational databases offer strong support for ACID transactions, which is vital for applications that require high levels of data accuracy and reliability.
- Advanced security features: RDBMSs typically come with advanced security features and access controls.
- Comprehensive tools and support: Due to their long presence in the market, relational databases have a wide range of tools, extensive documentation, and strong community and vendor support.
Cons
- Scalability challenges: Scaling a relational database typically requires vertical scaling (adding more powerful hardware), which is expensive and has its limits.
- Rigid outline design: The predefined schema of an RDBMS can make it less flexible in adapting to changes in the data structure. Significant effort is needed to modify existing schemes.
- Performance issues with large volumes of data: RDBMSs can face performance bottlenecks when handling very large volumes of data or high-velocity data, such as that found in big data applications.
- Complexity in handling unstructured data: Relational databases are not inherently designed to handle unstructured or semi-structured data.
- Cost and resource intensive: Maintaining and scaling an RDBMS can be resource-intensive and expensive, especially for large databases that require high-performance hardware.
Choosing between a relational and NoSQL cloud database
With their high consistency, structured data integrity, and transactional support, relational databases are ideal for situations where data integrity and order are paramount. They are useful in scenarios that require complex queries and precise data management. However, they face scalability challenges and are less flexible when data is constantly changing rapidly.
In contrast, NoSQL databases offer unparalleled scalability and flexibility in data modeling. NoSQL databases are also better suited to handling unstructured data, making them suitable for applications that require rapid development and handling large volumes of various types of data.
However, before making the decision to migrate to NoSQL, you should keep in mind that while they excel in scalability and flexibility, NoSQL databases often have a steeper learning curve due to a lack of standardization and can present challenges in ensuring data consistency and transactional support. .
Top Relational and NoSQL Cloud Databases to Consider
No two relational or NoSQL cloud databases are the same. They are all unique and work best with particular use cases. Below are some databases to consider for your organization.
NoSQL databases
- AmazonDynamoDB It is best for organizations or projects that demand a highly reliable and scalable NoSQL database with minimal maintenance needs. It is commonly implemented in web applications, games, mobile applications, Internet of Things, and many other applications.
- MongoDB Atlas is a fully managed NoSQL cloud service. It works best in applications that require a flexible schema for diverse and rapidly changing data formats, particularly in web and mobile and IoT applications.
- cassandra apache It is a good bet if you have a scenario that demands high flexibility and fault tolerance. It has been successfully deployed in multiple data centers and real-time big data applications.
- sofa base is your go-to NoSQL database if you work with interactive applications that demand high performance and low latency, such as mobile and edge computing.
Relational databases
- oracle cloud It is best for large-scale enterprise applications that require strong performance, security, and reliability.
- Microsoft SQL Server is ideal for organizations looking for a comprehensive relational database solution with strong integration with Microsoft products and services.
- PostgreSQL It is suitable for organizations looking for an open source RDBMS with a strong emphasis on standards compliance and extensibility.
