Data is now considered to be one of the most valuable assets of any organization. It makes transactions within a business easier and facilitates a smooth flow of operations. It is also a key decision-making tool as organizations are relying on evidence-based decision-making more than ever before. As a result, every organization is looking for a way to store its data in the most efficient way possible and SQL vs NoSQL is one of the most critical questions they are required to seek an answer for.
When choosing a modern database between SQL vs NoSQL companies usually find themselves in a puzzle. Although the two are viable options for businesses, there are key differences between them that must be considered. It is after considering these differences that an organization should be able to tell which database would be suitable for their requirements.
To put it briefly, NoSQL Databases excel in their ability to store data in a non-structured form as documents or key-value pairs. They allow for denormalized storage. SQL Databases, on the other hand, require the data to be stored in a structured and normalized way. As you’ll see, we’ll explore these segments in-depth in this guide.
This article will help you understand the various factors that drive the SQL vs NoSQL decision and make a choice based on your business and data requirements. Let’s get started.
Table of Contents
What is SQL Database?
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SQL stands for Structured Query Language and it’s the standard language for working with Relational Databases or SQL Databases. In SQL Databases, relationships are defined in the form of tables.
SQL can be used to create database objects and insert, update, delete, or query database records. Note that SQL is not limited to only that, but it can be used for complex tasks like optimizing and maintaining databases or performing Data Analysis. Some popular examples of SQL Databases include MySQL, Sybase, Microsoft SQL Server, Oracle, etc.
SQL Databases strictly comply with ACID guarantees and hence are a good choice for transactional data. These Databases are mainly based on a single node design.
It is safe to say that most SQL Databases prioritize consistency and availability over partition tolerance. This means SQL Databases are not very good at handling a large amount of data since the data have to be stored in a single system or need constant babysitting because of their limitations when it comes to multi-node operation.
That said, lately, most of the popular ones have been adding cluster support through the use of Sharding. But none of them are as elegant as their multi-node NoSQL counterparts since partition tolerance is built into their foundation. Sharding leads to increased costs and requires close management.
What is NoSQL Database?
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NoSQL is a Non-relational Database which means that it doesn’t need a fixed schema, scales easily, and cannot perform joins. It is suitable for distributed data stores with huge data storage needs. Due to this, NoSQL Databases are used for big data and in real-time web apps.
Since NoSQL Databases don’t store data in a relational schema, data attributes can be added on the fly without changing the structure of the entire table or adding redundant elements to the rest of the rows. Since there is no particular structure enforced by the database, they are also not very good at joining queries.
On the other hand, NoSQL Databases are great at scaling horizontally and partition tolerance is built into their foundation. NoSQL Databases do well in scenarios where a sub-second response time for high data volume is required. NoSQL Databases achieve this by compromising consistency and referential integrity.
NoSQL Databases find use in companies such as Facebook, Twitter, and Google that collect huge volumes of user data every day. NoSQL Databases use a wide range of database technologies capable of storing structured, unstructured, semi-structured, and polymorphic data.
NoSQL is an umbrella term to describe a whole set of databases that do not conform to the structured data format. It consists of the following:
- Document Databases
- Key-Value-Based Databases
- Column-Oriented Databases
- Graph Databases
1. Document Databases
Document Databases store data as objects in JSON form. MongoDB is an excellent example of such a database. Documents are considered independent units. They allow for seamless mapping from the object world of programming languages to data storage.
2. Key-Value-Based Databases
Key-Value-based Databases store data as a collection of key-value pairs. While they are not very popular in persistent storage, they deserve a mention here because of their widespread use in modern architecture. Key Value-based storage solutions are widely used as caching providers.
They are also used in cases where quick data sharing across multiple services is required. Redis, Memcache, etc are examples of Key-Value-based Databases.
3. Column-Oriented Databases
Column-Oriented Databases store data as a collection of columns and perform great when specific columns are accessed. Data rows can extend across multiple nodes or partitions in these databases.
They work based on the assumptions are rows are large enough to scale across multiple nodes and all columns are accessed rarely together. Hbase and Cassandra are good examples of Column-oriented Databases.
4. Graph Databases
Graph Databases store data as nodes and relationships. They help users to express complex relationships that exist between data elements and query them using specialized Graph Query Languages. Neo4j, Titan, etc are good examples of Graph databases that can be scaled horizontally.
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Factors that Drive the SQL vs NoSQL Decision
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Now that you have a basic idea of both worlds, let us attempt to answer the Relational Database vs NoSQL question of how to make a decision. There is no one-size-fits-all answer here and the decision has to be taken based on the attributes of your use cases.
The following are the key attributes that drive the Relational Database vs NoSQL decision:
SQL vs NoSQL: Usage
SQL Databases are also referred to as Relational Databases. Relational Databases use Structured Query Language (SQL) queries to perform the required operations on the data.
SQL queries have support for complex operations such as aggregations, joins, etc., and hence, SQL Databases are the preferred choice for Online Analytical Processing (OLAP) systems and for storing data in which a change in storage format or schema is not expected.
NoSQL Databases combine various database technologies that are developed to meet the requirements of modern-day applications of dynamic data formats and distributed storage.
SQL vs NoSQL: Data Structure
All SQL Databases have a fixed data format i.e. data is stored in the forms of rows and columns in tables. These databases follow a fixed schema which means that the data being inserted in the table should be in that fixed format.
A single SQL Database can have multiple tables that have relations between them. A sample SQL Database is as follows:
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NoSQL Databases are considered to be more flexible in terms of data format. Each record in a NoSQL database can have a completely different format which can be decided on the basis of what kind of data is to be stored at that moment.
NoSQL Databases are hence considered to be more suited for modern-day applications that have very dynamic data storage requirements. A sample NoSQL Database is as follows:
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Each document in the above example can be considered a record in the database. It can be observed that all documents follow a different structure to store data. This dynamic structure of data storage is considered to be one of the most significant advantages of using NoSQL Databases.
SQL vs NoSQL: Query Language Support
All SQL Databases have support for a common query language called Structured Query Language (SQL). SQL can be used easily to perform the required CRUD (Create, Read, Update, and Delete) operations.
SQL is considered to be one of the most versatile and widely used query languages. It allows users to perform complex queries that can easily be understood by most developers. A sample SQL query to insert a record into a table is as follows:
INSERT INTO EMPLOYEES (Name, Age, PhoneNumber)
VALUES (“EMP1”, 21, “1234567890”);
NoSQL Databases do not have a common format to query data or a common query language. Each database follows a different format for performing the required operation.
A sample query to insert a record in a NoSQL Database, MongoDB is as follows:
db.employees.insert({
“name”:”Emp1”,
“Age”:21,
“phoneNumber”: “1234567890”
})
Another sample query to insert the same record in a NoSQL Database, HBase is as follows:
put ‘employees’, ’1’, ’Name’, ’Emp1’
put ‘employees’, ’1’, Age, ‘21’
put ‘employees’, ’1’, ’PhoneNumber’, ’1234567890’
Hence, it can be observed that the format to perform the same operation varies with the NoSQL Database used whereas that is not the case with SQL Databases that use a common query language for all Relational Databases.
However, NoSQL Database queries are known for their flexibility in data handling which means that they do not require a fixed format or schema for data records and hence, can be adjusted for each record in the database which is not the case with SQL queries that requires a fixed format for the data.
SQL vs NoSQL: Workload Volume
As discussed above, NoSQL Databases prioritize partition tolerance. This means they are great at handling large amounts of data and executing typical queries over them. So if your application requires data processing over TBs of data, it is better to go with a NoSQL Database from the start itself.
This is not to say that SQL Database systems do not support TBs of data. Most of them like Oracle can very well handle TBs of data. But if most of your queries touch upon this entire data, then it may be better to think about a NoSQL distributed alternative.
Another critical factor is that some of the NoSQL Databases need a minimal hardware level to perform acceptably. For example, Cassandra performs best when there is at least a 3-node cluster. But if your data is not enough to fill even a single node, you may have to spend more in the initial phase without using up the resources effectively.
SQL vs NoSQL: Scalability
This is considered to be one of the most important factors for most businesses today. Businesses want to ensure that their databases can scale as per the storage and compute requirements and perform regular replication to provide wider access.
SQL Databases are considered to be Vertically Scalable, also known as Scaling Up. Vertical Scaling means that the system is capable of scaling up RAM, CPU, SSD capacities exponentially and storage to some extent to ensure that the required service can be provided to the users.
This means that all CRUD (Create, Read, Update and Delete) operations can be performed easily. Although this has a lot of advantages, the framework of these databases cannot be altered and is hence considered to be restricted on that front.
NoSQL Databases are considered to be Horizontally Scalable, also known as Scaling Out. Horizontal Scaling means that more machines are added to the existing resources to fulfill storage requirements.
This is considered to be suitable for businesses that are constantly altering their frameworks and adding more servers to expand their storage capacity and improve their setup.
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SQL vs NoSQL: Properties
All SQL Databases follow the ACID (Atomicity, Consistency, Isolation, Durability) properties. A description of these properties is as follows:
- Atomicity: Each transaction is considered to be a single unit. This means that either the entire transaction is completed or it is not processed at all.
- Consistency: Each transaction only takes the database from one valid state to another.
- Isolation: Multiple independent transactions acting on different data can take place simultaneously on the database without affecting each other.
- Durability: Once a transaction is completed, its results are saved in the database and will continue to stay in that state even if the system fails.
NoSQL Databases on the other hand follow the CAP (Consistency, Availability, Partition Tolerance) theorem. A description of these properties is as follows:
- Consistency: All clients connected to a database see the same data which means that once any data is written or updated in the database, the same operations should be performed on its replications as well.
- Availability: Any request from a client gets at least some response from the database.
- Partition Tolerance: The cluster continues to perform the required operations even if some nodes go down.
The CAP Theorem states that NoSQL Databases are capable of providing only two out of the three CAP properties.
SQL vs NoSQL: Storage Requirements
SQL Database systems perform best when data can be expressed in a denormalized form. This allows you to optimize your storage requirements.
Comprehensive SQL layers with complex joining abilities allow the database to make the most of denormalized data. If your data cannot be expressed like this, your use case may be better served using a NoSQL Database.
On the other hand, if your data has well-formed relationships that can be used in denormalizing into multiple levels, you should consider using an SQL database.
SQL vs NoSQL: Write Performance Requirements
NoSQL Databases compromise consistency to achieve fast write performance. SQL Databases offer to write safely with consistency but at the expense of a bit of speed. Eventual consistency may be a strict nonstarter in some use cases but may be acceptable in others.
A good answer to the question ‘Can we afford to let go of strict consistency for faster writes ?’ can help you arrive at the SQL vs NoSQL decision quickly.
SQL vs NoSQL: Read Requirements
RDBMS possesses a great ability to query data and execute complex joins. NoSQL Databases perform best when data is stored in the same form in which they are to be consumed.
For example, let’s say you are creating a reporting solution. You can choose to implement it by storing data for specific reports in different tables and accessing it through a simple select statement, in this case, you are better off with a NoSQL Database.
The other choice is to store the base data in a small number of related tables and execute various queries and aggregate them to form different reports and this use case points to using a SQL Database.
SQL vs NoSQL: Infrastructure Constraints
NoSQL Databases are well known for their ability to run using cheap general-purpose hardware and scale horizontally. Since the cost of a high-end special-purpose instance is more than multiple cheap general-purpose instances, there is the possibility of cost advantage in case you use a NoSQL Database.
This becomes valid only when your data volume is significant enough for a distributed database to make sense. For handling TBs of data, SQL Databases often require high-end special-purpose hardware.
Now with the advantages of SQL vs NoSQL Databases in place, let’s discuss the limitations of both.
Limitations of Using SQL Databases
The following are the drawbacks associated with SQL Databases:
- They are rigid data models that need a careful up-front design for performance. To change a schema, downtime is inevitable.
- Horizontal scalability is challenging. It’s either completely unsupported, or supported by immature technologies, or supported in an ad-hoc way.
- Non-distributed SQL engines have a single point of failure that can only be mitigated by failover and replication techniques.
Limitations of Using NoSQL Databases
The following are the drawbacks of using NoSQL Databases:
- NoSQL Databases do not provide full support for ACID properties.
- Most NoSQL Databases were designed to allow for scalability with no data consistency in consideration.
- NoSQL Databases do not have support for complex querying operations such as joins, aggregations, etc., and hence cannot be considered suitable for analytical purposes.
To summarise, the key differences between SQL and NoSQL are:
| Parameter | SQL | NoSQL |
| Relational/Non-Relational | SQL is a relational database | NoSQL is a non-relational database |
| Query Language | Uses structured query language | No declarative query language |
| Used for: | SQL Databases are used for OLAP systems | NoSQL Databases were developed in response to demands presented for the development of modern applications |
| Schema | SQL Databases have a pre-defined schema | NoSQL Databases use dynamic databases for unstructured data |
| Scalability | Vertically scalable | Horizontally Scalable |
| Type | These are table-based databases | NoSQL databases can be document based, key-value pairs, or graph databases |
| Hierarchical data storage | SQL Databases are not suited for hierarchical data storage | NoSQL Databases are more suited for hierarchical data storage as they support |
| Examples | Oracle, Postgres, and MS-SQL | MongoDB, Redis, Neo4j, Cassandra, Hbase |
Conclusion
This article helped you understand the various factors that drive the SQL vs NoSQL decision. There’s no denying the fact that your final decision will be based on various factors and it cannot be said that one database is better than the other.
Most modern businesses today use multiple databases for their operations. This results in a complex situation since it can be a complicated task if a common analysis integrating data from all these databases has to be performed.
A Data Integration solution has to be built first that can integrate all data from these databases and store it in a centralized location. Businesses can either choose to make their own Data Integration solutions, which is a challenging task or choose to use a simple 3-click Data Pipeline Creation Solution like Hevo Data.
Hevo provides a Cloud-based ETL Solution that helps you transfer data from most of the popular SQL as well as NoSQL Database Systems. It is a No-Code Data Pipeline Automation Solution that helps you to replicate and load data using most of the widely used source and target database combinations.
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Hevo enables the lowest time to production for such copy operations, allowing developers and analysts to focus on their core business logic rather than waste time on the configuration nightmares involved in setting these up. You can find details on Hevo’s pricing here.
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