The debate between Relational Database Management Systems (RDBMS) and NoSQL databases in the cloud ecosystem has been ongoing for years. Each technology has its strengths and weaknesses, and misconceptions often surround their capabilities. In this blog, we’ll debunk common myths surrounding RDBMS and NoSQL databases by exploring practical use cases in a cloud environment.
Myth 1: “RDBMS is outdated and cannot scale in the cloud.”
Reality: RDBMS systems, when properly configured, can scale horizontally and vertically in the cloud.
Practical Use Case: Consider a scenario where an e-commerce platform experiences a surge in traffic during a flash sale. A few years back, we would have had just one option – ‘No SQL’ specially when it comes to scaling horizontally. It’s a common myth that RDBMS database can just scale vertically by increasing components like RAM, SSD, or CPU on a single server. But RDBMS databases are now well capable of scaling horizontally as well by addition additional servers. By leveraging cloud-based RDBMS scaling capabilities, such as auto-scaling and read replicas, the system can efficiently handle increased load while maintaining data integrity.
Myth 2: “NoSQL is always the best choice for schema flexibility.”
Reality: While NoSQL databases excel in handling unstructured data, modern RDBMS systems support schema flexibility through features like JSON data types.
Practical Use Case: Imagine a content management system where users can define custom fields for articles. Flexibility in data model and less schema dependency is something that was referred always to be a unique feature of NoSQL database. But this is not right. A cloud-based RDBMS with JSON / JSONB support allows for flexible schema design without sacrificing the benefits of relational integrity. RDBMS now not only supports the complex and flexible data models to be stored in JSON / JSONB format, but also it allows you to index specific node / node combinations to achieve great performance.
Myth 3: “NoSQL is the only option for high write throughput.”
Reality: Both RDBMS and NoSQL databases can achieve high write throughput in the cloud, depending on the chosen database engine and architecture.
Practical Use Case: In a real-time analytics platform, where capturing and processing vast amounts of event data is crucial, a NoSQL database might be selected. However, modern cloud-based RDBMS solutions with optimized write patterns can deliver comparable performance. Modern RDBMS solutions are capable of handling billions of write operations targeted to an instance from multiple microservice and event queues in parallel.
Myth 4: “RDBMS is not suitable for handling big data.”
Reality: Cloud-based RDBMS systems are well-equipped to handle big data scenarios through features like partitioning and sharding.
Practical Use Case: Consider a financial institution managing large volumes of transaction data. With proper cloud-based RDBMS configurations, the system can efficiently store, query, and analyze vast datasets while maintaining ACID (atomicity, consistency, isolation, and durability) compliance. Remember – companies like Google, still use structured data to search the web to understand page content. There is no doubt about the robust querying capabilities of RDBMS compared to NoSQL. With the revamped features of RDBMS to enable it to handle- Big Data, this querying capabilities gives it an edge.
Myth 5: “NoSQL is always more cost-effective in the cloud.”
Reality: The cost-effectiveness of RDBMS vs. NoSQL depends on factors like data structure, query patterns, and scalability requirements.
Practical Use Case: In a scenario where data relationships are well-defined, and the workload involves complex queries, a cloud-based RDBMS might offer better cost efficiency due to its optimized query execution and storage mechanisms.
The choice between RDBMS and NoSQL in the cloud ecosystem should be driven by specific use case requirements rather than myths. RDBMS is always known for its structured relation-based architecture where in NoSQL offered flexible schema for unstructured data model. However, both technologies have evolved and enhanced a lot to meet the demands of modern applications. Understanding their strengths and limitations is crucial for making informed decisions today. By exploring practical use cases, we can debunk common misconceptions and pave the way for effective database solutions in the cloud.