According to LinkedIn, there are around 2,00,000+ SQL and more than 30,000 NoSQL jobs available in India. Does this mean SQL is better than NoSQL? No, both databases have their own advantages and disadvantages. So, choosing the database completely depends on the requirements of the projects.
This article will provide you with an in-depth comparison of both databases that will help you decide which database is best suited for you. Keep reading!
Check this video On NoSQL vs SQL:
What is SQL?
Structured Query Language, or SQL, is a standard database language used to create, maintain, and retrieve information from a relational database. This sort of database stores data in tabular format, meaning data is spread in the form of rows and columns. Some of the key features of SQL that make it a popular database management system of all time are:
- They fit into most of the popular software stacks.
- The easy and familiar structure makes it a preferred option.
- It eliminates redundancy and synchronizes the data.
However, these benefits may not be sufficient for changing business requirements. NoSQL fills this gap.
What Is NoSQL?
A NoSQL database is a non-relational database, which means it does not use tabular schema to store the data. In NoSQL databases, we use storage models to store data, which are optimized to meet the specific requirements of the types of data to be stored. For instance, data can be stored as key/value pairs, JSON documents, or graphs with edges and vertices.
Despite the absence of a tabular structure, NoSQL has gained popularity because of the widespread adoption of databases like MongoDB, Cassandra, and HBase.
SQL vs. NoSQL: What Are the Differences?
Relational and non-relational are not just the differences between SQL and NoSQL; several factors set them apart. The following table will give a better understanding of the difference between these query languages:
| Parameters | SQL | NoSQL |
| Definition | Known as a relational database | Known as a non-relational database |
| Schema | Static schema | Dynamic schema |
| Representation | Represented as tables | Represented as key-value pair, graph database, wide-column stores, etc. |
| Scalability | Vertically scalable | Horizontally scalable |
| Complex Queries | Best for complex queries | Not so good for complex queries |
| Language | Uses a powerful standard language called Structured Query Language (SQL) | Language varies from database to database |
| Type | Table-based databases, document-based, key-value pairs, and graph databases | Document based, key-value pairs, graph databases, and table-based databases |
| Hierarchical Data Storage | More suitable for the hierarchical data store, as it supports the key-value pair method | Not suitable for hierarchical data storage |
| Variations | Multiple types, which include document databases, key-value stores, and graph databases | One type with minor variations |
| Open-Source | Open-source | A mix of open-source and commercial |
| Consistency | Depends on DBMS as some offer strong consistency, whereas others only offer eventual consistency, like Cassandra | Needs to be configured for strong consistency |
| Best Used for | For solving ACID problemsFor solving data availability problems | For solving data availability problemsFor solving ACID problems |
| Importance | Used when fast data is preferred over correct data | Used when data validity is preferred |
| Best Option | For scaling as per changing requirements | For supporting dynamic queries |
| Hardware | Commodity hardware | Specialized DB hardware, like Oracle Exadata |
| Network | Commodity network (ethernet, etc.) | Highly available network (Infiniband, FabricPath, etc.) |
| Storage Type | Commodity drive storage (standard HDDs, JBOD, etc.) | Highly available storage (SAN, RAID, etc.) |
| Best Features | High performance, easy-to-use, and flexible tool | Cross-platform support, free, and secure |
SQL or NoSQL: Which Is Better?
The choice between SQL and NoSQL does not depend on the advantages and disadvantages of these database systems; rather, it’s about the type of web applications you deal with and the results you expect from a query system.
Whenever people compare SQL with NoSQL, they state that NoSQL overpowers SQL and is better. You must know that this is a MYTH! Remember, none of them supersedes each other, and NoSQL is not a replacement for SQL but rather an alternative to it.
In reference to the differences between databases, one of the technology experts said, “One size does not fit all.” It means some projects and applications are better suited to SQL, while others are better suited to NoSQL.
In fact, some SQL databases are adopting features of NoSQL and working in collaboration. There have always been some rules when using databases, like MySQL (SQL) databases being used by PHP or .Net projects. Do not consider it a rule; you may use MongoDB (NoSQL) in your PHP application. Similarly, you can use SQL Server in Node.js applications instead of considering NoSQL as the only suitable fit.
Why Is NoSQL Preferred Over SQL?
In many cases, NoSQL is preferred over SQL because it offers more flexibility, scalability, and the ability to handle unstructured data. NoSQL allows you to store and access data more quickly and easily than SQL.
However, it does not mean SQL will be replaced entirely, as both databases have their use cases and strengths. Ultimately, the preference for a database completely depends on the particular organization. Organizations choose according to their specific needs and requirements.
Advantages and Disadvantages of SQL
Many companies use SQL because it follows an organized data storage model based on tables. As a result, SQL-dependent databases like MySQL, PostgreSQL, and Microsoft SQL Server continue to be widely used. While these principles hold, it is critical to consider both the benefits and downsides of SQL.
Advantages of SQL
- One of the primary advantages of SQL is easy data retrieval. What it means is that SQL provides a simple and easy method of retrieving and manipulating data from a database. For example, some SQL commands are SELECT, which is used to retrieve data from a database; INSERT, which is used to add new records in a database; UPDATE, which is used to modify existing records in a database; and DELETE, which is used to remove unwanted records from a database.
- SQL offers a uniform platform worldwide. It is a standard language that ensures consistency across various databases when interacting with relational databases.
- It can be used in programs on servers, PCs, and laptops, independent of any platform, and can be embedded with other applications, making it portable.
- It is an interactive language that is easy to learn and understand.
- Multiple data views are possible in SQL.
Disadvantages of SQL
- SQL has a difficult interface that can be uncomfortable for users dealing with the database.
- Some versions of SQL are costly, which makes them inaccessible to programmers.
- Due to hidden business rules, only partial control is given to the database.
Advantages and Disadvantages of NoSQL
NoSQL is majorly preferred by lots of businesses, the reason being that, unlike SQL, it does not follow a flat data storage model (table). Though these notions remain true, there are certain positives and negatives to NoSQL as a framework.
Advantages of NoSQL
- There is no constraint on the structure of the data to be stored.
- Integration with cloud computing gives NoSQL an edge.
- The dynamic and quick development of a database is possible.
- Huge amounts of data can be stored.
Disadvantages of NoSQL
- NoSQL databases are comparatively less developed as compared to traditional relational databases.
- In general, NoSQL databases prioritize scalability and flexibility over complex query capabilities. This indicates that NoSQL databases may lack the querying language and functionality provided by SQL databases.
- In some complex cases, NoSQL leads to data inconsistency and poor performance.
- Improvements are yet to be made for cross-platform support.
SQL Tables vs. NoSQL Documents
SQL provides a storage database in the form of a relational table model, i.e., rows and columns. For instance, you have a table named training_course with the below-mentioned data of two online training companies. Each row has a different record. In this table, the data type of each field is fixed, and you cannot insert a number in place of a string.
| Course Code | Title | Company | Training Format | Price (in $) |
| 7654389 | Tableau Training | Intellipaat | Online | 150 |
| 3456789 | Big Data Training | IBM | Online | 200 |
On the other hand, NoSQL databases make use of field-value pairs in a document, similar to JSON.
For instance,
{
Course Code: 7654389
Title: “Tableau Training”,
Company: “Intellipaat”,
Training format: “Online”,
Price (in $): 150
}
Such documents are stored in a collection in NoSQL database as they are stored in a table in SQL. But, you can store any type of data you wish in any of the documents of NoSQL.
{
Course Code: 7654389
Title: “Tableau Training”,
Company: “Intellipaat”,
Training format: “Online”,
Price (in $): 150,
Rating: 5/5
Review:
[
{ name: “Alex” , text: “The course was very helpful in preparing for Tableau Certification.” },
]
}
Unlike SQL databases, which are strict to allow any changes in data type and have a fixed data template, NoSQL employs more flexibility. However, sometimes a lot of changes lead to consistency issues.
Now, we will talk about a few of the most important criteria in SQL and NoSQL: schema structure and data integrity.
Schema Structure
The tables in the SQL database refer to the schema structure, which contains information about the following parameters:
Primary key: It is a unique field that can help you identify a record uniquely; for instance, Course Code in the above table.
Relationship: It refers to logical links between two or more data fields in a table, functionality like Triggers, Views, Stored Procedures, Joins, etc.
SQL has a schema structure, while NoSQL is schema-less. Most SQL developers decide and define the schema structure, including any of these functionalities in their table, before implementing or applying any business logic to modify and/or update the table. This is because it becomes complex to make significant changes later.
On the contrary, as mentioned, NoSQL is formative, and the data can be modified and added anywhere at any time. You need not mention a predefined document design. In MongoDB, db.table_course.insert command lets you create a new document if it wasn’t created before. It will by default add a unique _id to every document present in a collection.
Data Integrity: An Important Criterion
While working with two SQL tables, you can relate them to a foreign key field. This refers to data integrity. If we have a project table, the project_id will be the foreign key, and the rules could be defined as follows:
- It should be ensured that each training course has a project_id matching that in the project table.
- Do not let users delete projects if there are one or two training courses assigned to them.
In this case, it is complex for developers and database administrators to edit or delete records, resulting in inconsistent databases. There is no data integrity operation available with NoSQL databases. Each document acts as an independent source of information about an item.
Database Scaling
In most cases, SQL databases are scaled vertically. This means that by increasing the CPU, RAM, or SSD capacity on a single server, you can increase the load on it.
On the other hand, NoSQL databases are scaled horizontally, which means they have the ability to handle increased demand or usage by adding more resources or components in a parallel manner. Due to its larger capacity, NoSQL is a preferred database for organizations with large and frequently changing data.
Use Cases
NoSQL databases work better with unstructured data, such as documents or JSON, whereas SQL databases work better with multi-row transactions. Relational databases are the foundation of many legacy systems, which also frequently use SQL databases.
For user-oriented applications that involve several join operations, you may choose to use an SQL database. You can improve data compatibility and achieve ACID qualities with the aid of the SQL schema. These databases come in handy when you need to locate information quickly to finish a task.
For applications that require dynamic data without join operations, a NoSQL database might be appropriate. Applications with missing data sets that won’t affect business efficiency are also better suited for NoSQL.
SQL Database Systems
Listed below are a few of the most widely used SQL database systems:
MySQL
- Cost-free and open-source
- Well-known database with a sizable community, a lot of testing, and stability
- Accommodates all popular platforms
- Sharding and replication are available.
- Includes a large number of use cases
Oracle
- Among the most costly database solutions
- Suitable for every kind of operating system
- Easy upgrades
- Work with large databases
- Ideal for businesses and establishments with heavy workloads
Microsoft SQL Server
- The SQL dialect used in a commercial database created and maintained by Microsoft is called T-SQL.
- Compatible with only Windows and Linux
- Easy to use
- Effective for small and medium-sized businesses seeking a commercial database
PostgreSQL
- Open-source and free of cost
- Suitable for a wide range of operating systems
- Uses only SQL
- High conformance with ACID
NoSQL Database Systems
Listed below are a few of the most widely used NoSQL database systems:
MongoDB
- The most widely used NoSQL database
- Dynamic schema
- Horizontally scalable
- Ideal for businesses with a lot of unstructured data or those undergoing rapid expansion
Cassandra
- Open-source
- Manages a lot of data across commodity servers
- Scalable
- Follows peer-to-peer architectural design
Conclusion
Ultimately, the choice between SQL and NoSQL databases cannot be concluded based on the differences between them but must be based on the project’s requirements. If your application has a fixed structure and doesn’t need frequent modifications, SQL is a preferable database.
Conversely, if you have applications where data is changing frequently and growing rapidly, like in Big Data Analytics, NoSQL is the best option. SQL is still a widely used database and will not easily die out, even with the introduction of other database technologies.