The question, Relational Database vs NoSQL is one of the most critical questions a solution architect has to answer while designing an application in the modern Big Data era. 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. Relational Databases, on the other hand, require the data to be stored in a structured and normalized way.
While it may seem that NoSQL Databases save a lot of time in database definition initially in the development process, the fact remains that a well-defined database schema with a Relational Database can give a sizable performance advantage in some cases. This article will highlight the key factors to keep in mind while comparing Relational Databases and NoSQL Databases to help you make a decision.
Relational Databases excel at their ability to store structured data. They provide comprehensive querying layers and store data in the minimum footprint possible by denormalizing the data. Since data is denormalized, accessing them requires complex joins.
Relational Databases have existed for ages and they are proven in a wide variety of applications. There are several licensed and open-source Relational Databases out there and all of them are mature enough to be considered for production-grade applications.
Relational Databases strictly comply with ACID guarantees and hence are a good choice for transactional data. Relational Databases are mainly based on a single node design.
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.
It is safe to say that most Relational Databases prioritize consistency and availability over partition tolerance. This means Relational Databases are not very good at handling a large amount of data since the data they have to be stored in a single system or need constant babysitting because of their limitations when it comes to multi-node operation.
What are NoSQL Databases?
NoSQL Databases are great at storing semi-structured or non-structured data since they don’t enforce a concrete schema for tables. This means 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 join queries.
NoSQL Databases recommend data to be stored in a format in which it will be frequently accessed. This helps in defining the database very close to the UI layer or where the data will be actually used or reported.
NoSQL Databases are great at scaling horizontally and partition tolerance is built into their foundation. NoSQL Databases do well in scenarios where sub-second response time for high data volume is required. NoSQL Databases achieve this by compromising consistency and referential integrity.
Most NoSQL Databases support only eventual consistency and are hence not a great choice for transactional operations. Lately, databases like MongoDB have found some success in breaking this barrier. Here is the article on MongoDB vs MySQL.
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 as independent units. They allow for a 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 the widespread use in modern architectures. 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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- Secure: Hevo has a fault-tolerant architecture that ensures that the data is handled in a secure, consistent manner with zero data loss.
- Schema Management: Hevo takes away the tedious task of schema management & automatically detects schema of incoming data and maps it to the destination schema.
- Minimal Learning: Hevo with its simple and interactive UI, is extremely simple for new customers to work on and perform operations.
- Hevo Is Built To Scale: As the number of sources and the volume of your data grows, Hevo scales horizontally, handling millions of records per minute with very little latency.
- Incremental Data Load: Hevo allows the transfer of data that has been modified in real-time. This ensures efficient utilization of bandwidth on both ends.
- Live Support: The Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
- Live Monitoring: Hevo allows you to monitor the data flow and check where your data is at a particular point in time.
Factors that Drive the Relational Database Vs NoSQL Decision
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 attributes of your use cases. The following are the key attributes that drive the Relational Database vs NoSQL decision:
Relational Database Vs NoSQL: Schema Flexibility
The biggest advantage offered by NoSQL Databases is the flexibility of schema. They allow for attributes to be added or forgotten at will. So the very question that you should ask yourself is whether your use case can take advantage of this schema flexibility.
Let’s say for example you are trying to implement an IoT platform that stores data from different kinds of sensors, you will be better off choosing a NoSQL Database because you do not have an upfront idea about the attributes of your data and it is bound to change as the application evolves.
On the other hand, if you are implementing a simple web application with all the user attributes known upfront, there really is no reason to look beyond RDBMS.
Relational Database 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 Relational 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.
Relational Database Vs NoSQL: Data Consistency
Relational Databases are great at enforcing consistency. NoSQL Databases mostly go by eventual consistency when it comes to writes. This means, there is a chance that your application will read old data till the time writes are propagated to all the nodes. If your application cannot afford such scenarios, you should use a classic Relational Database.
This limitation of NoSQL Databases makes them a non-starter for transactional loads. Databases like MongoDB have started providing transactional support recently and even then, it is limited to short-duration transactions.
Relational Database vs NoSQL: Storage Requirements
Relational 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.
Relational Database 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 RDBMS vs NoSQL decision quickly.
Relational Database 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 access 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 Relational Database.
Relational Database 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, Relational Databases often require high-end special-purpose hardware.
|NoSQL Database has no fixed schema.
|Relational Database has a fixed schema.
|NoSQL Database is only eventually consistent.
|Relational Database follows acid properties. (Atomicity, Consistency, Isolation, and Durability)
|NoSQL databases don’t support transactions (support only simple transactions).
|Relational Database supports transactions (also complex transactions with joins).
|NoSQL Database is used to handle data coming in high velocity.
|Relational Database is used to handle data coming in low velocity.
|The NoSQL?s data arrive from many locations.
|Data in relational database arrive from one or few locations.
|NoSQL database can manage structured, unstructured and semi-structured data.
|Relational database manages only structured data.
|NoSQL databases have no single point of failure.
|Relational databases have a single point of failure with failover.
|NoSQL databases can handle big data or data in a very high volume .
|NoSQL databases are used to handle moderate volume of data.
|NoSQL has decentralized structure.
|Relational database has centralized structure.
|NoSQL database gives both read and write scalability.
|Relational database gives read scalability only.
|NoSQL database is deployed in horizontal fashion.
|Relation database is deployed in vertical fashion.
Choosing one between Relational and NoSQL Databases is often a tough challenge and you will have arguments in favor of both in most cases. Even in the modern Petabyte-scale data architecture, Relational Databases find their place in specific scenarios. This is why most data architectures have both Relational and NoSQL Databases splitting the storage duty and this is where Relational Database vs NoSQL becomes Relational Database and NoSQL.
Whether you choose to pick one or choose to split responsibilities between the two paradigms, the success of your architecture often depends on having access to data transfer tools that can work between these systems and external data sources. Hevo provides a cloud-based ETL tool that helps you transfer data from most of the popular Relational as well as NoSQL systems.
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Hevo is a No-code data pipeline that helps you to replicate and load data using most of the widely used source and target database combinations. Check out Hevo’s 100+ integrations here. 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.
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