Hybrid Database
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. To learn more, check out our insightful piece on Structured vs 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. You can discover more insights in our detailed comparison article on 'DynamoDB vs MongoDB.'
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.
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.
Recap
| Category | SQL | NoSQL |
| Data storage models | Data is stored in tables with rows and columns | JSON documents, key-value pairs, tables with dynamic columns, nodes and edges, etc. |
| Database schemas | Pre-defined, fixed schemas | Dynamic, flexible schemas |
| Query language | SQL | Varies depending on the data model |
| Data types | Structured data | Structured, unstructured and semi-structured data |
| Transactions | ACID compliant | Varies depending on the database |
| Performance | Built to record transactions and run complex queries | Built for fast performance and high availability |
| Scalability | Vertical scaling | Horizontal scaling |