Introduction
When it comes to managing the database, you have two popular options: SQL and NoSQL. Both these serve as the backbone of data management for several applications. However, each of these come with their own set of features, and benefits making them apt for different objectives.
Hence, choosing the best option from either of SQL and NOSQL becomes important. In this blog, we will be unfolding the key differences between the two while discussing the features of each.
What is SQL?
SQL or Structured Query Language It is used to manage and manipulate relational databases. These are also known as Relational Database Management Systems (RDBMS). One of the defining features of SQL is that it determines the organization of data within tables, thus making them suitable for applications that require consistent structure
Key Features
- Structured Data: SQL databases are designed for structured data, where each entry follows a specific format.
- Predefined Schema: Users must define the schema before inserting data, which can make changes difficult later on.
- ACID Compliance: SQL databases adhere to ACID (Atomicity, Consistency, Isolation, Durability) properties, ensuring reliable transactions.
- Complex Queries: SQL allows for complex queries using JOIN operations to retrieve related data from multiple tables.
What is NoSQL?
Unlike SQL, NoSQL is suitable for unstructured data. It stands for “Not Only SQL.” It refers to a variety of database systems that do not use the traditional relational model.
NoSQL is useful for storing semi-structured and unstructured data in different formats like graphs, key-value pairs, etc. This flexibility adds versatility to NoSQL, thus making it suitable for a host of applications.
Key Features of NoSQL Databases
- Flexible Schema: NoSQL databases allow for dynamic schemas, meaning that the structure can change over time without significant overhead.
- Scalability: Most NoSQL databases are designed to scale horizontally by adding more servers as needed.
- High Performance: They are optimized for high-speed read and write operations, making them suitable for big data applications.
- Variety of Data Models: NoSQL supports various data models, including document-based (like MongoDB), key-value stores (like Redis), column-family stores (like Cassandra), and graph databases (like Neo4j).
Key Differences Between SQL and NoSQL
The above section discusses the features of SQL and NoSQL, and we can conclude that SQL is suitable for structured data while NoSQL is apt for unstructured data. Besides this difference, there are other features that differentiate SQL and NoSQL; these are listed below:
SQL vs NoSQL: Which is Faster?
When it comes to the application part, SQL and NoSQL serve different purposes. These are mentioned below:
- SQL Databases: One can use SQL for complex queries or when working with structured data. Also, if you require data consistency, the SQL takes the lead. However, when the volume of data increases, then scalability is a concern with SQL and thus, you may require infrastructure upgrade.
- NoSQL Databases: Unlike SQL, NoSQL works well with unstructured data, thus making it suitable for handling large volumes of data. Since NoSQL can accommodate different servers, it can easily handle bulk influx of data, making it faster.
When to Choose SQL?
However, NoSQL is faster and can function seamlessly even when there is a larger flow of data. SQL databases are well-suited for use cases where:
- There is data consistency and transactional integrity is crucial. Hence it becomes imperative in the banking system, CRM, etc.
- SQL requires a well-defined schema
- SQL becomes applicable where complex queries are involved
When to Choose NoSQL?
NoSQL databases are a better choice when:
- It works best when you are handling a large volume of unstructured data like social media.
- Handling large volumes of unstructured datasets.
- You need real-time data processing.
Conclusion
The above discussion highlights that both SQL and NoSQL have their own set of benefits. While SQL is suitable for structured data, NoSQL is more helpful in the case of unstructured data. Hence, when it comes to choosing between the two, it becomes important to assess the objective and requirement.
You can learn about such tools and techniques in Data Science courses and put your skills to the test while pursuing a Data Science internship. Also, one can use these skills while studying Data Science case studies. The future is all about data and mastering such key skills will help you excel professionally.
