When choosing a modern database, one of the biggest decisions is picking a relational (SQL) or non-relational (NoSQL) data structure. While both are viable options, there are key differences between the two that users must keep in mind when making a decision.

Here, we break down the most important distinctions and discuss the best SQL and NoSQL database systems available.

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Table of Contents

  1. Database Architecture
  2. Database Schemas and Query Language
  3. Database Scaling
  4. Data Structure
  5. Ideal Use Cases
  6. SQL Database Systems
  7. NoSQL Database Systems

Database Architecture

At the most basic level, the biggest difference between these two technologies is that SQL databases are relational, while NoSQL databases are non-relational. 

Database Schemas and Query Languages 

SQL databases use structured query language and have a pre-defined schema for defining and manipulating data. SQL is one of the most versatile and widely used query languages available, making it a safe choice for many use cases. It’s perfect for complex queries. However, SQL can be too restrictive. You have to use predefined schemas to determine your data structure before you can work with it. All of your data must follow the same structure. This process requires significant upfront preparation. If you ever wanted to change your data structure, it would be difficult and disruptive to your whole system. 

NoSQL databases have dynamic schemas for unstructured data, and the data is stored in many ways. You can use column-oriented, document-oriented, graph-based, or KeyValue store for your data. This flexibility means that: 

  • You can create documents without having to first define their structure
  • Each document can have its own unique structure
  • The syntax can vary from database to database
  • You can add fields as you go

Database Scaling 

SQL databases are vertically scalable in most situations. You’re able to increase the load on a single server by adding more CPU, RAM, or SSD capacity. NoSQL databases are horizontally scalable. You’re able to handle higher traffic by sharding, which adds more servers to your NoSQL database. Horizontal scaling has a greater overall capacity than vertical scaling, making NoSQL databases the preferred choice for large and frequently changing data sets. 

Data Structure

SQL databases are table-based, while NoSQL databases are document, key-value, graph, or wide-column stores. 

Some examples of SQL databases include MySQLOraclePostgreSQL, and Microsoft SQL Server. NoSQL database examples include MongoDB, BigTable, Redis, RavenDB Cassandra, HBase, Neo4j, and CouchDB.

Ideal Use Cases

SQL databases are better for multi-row transactions, while NoSQL is better for unstructured data like documents or JSON. SQL databases are also commonly used for legacy systems that were built around a relational structure. 

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SQL Database Systems

Now that you know the key differences between SQL and NoSQL databases, it’s time to explore the different options available for your workloads. 

MySQL

  • Free and open-source 
  • Extremely established database with a huge community, extensive testing, and lots of stability
  • Available for all major platforms
  • Replication and sharding are available
  • Covers a wide range of use cases 

Oracle

  • Commercial database with frequent updates, professional management, and excellent customer support
  • Procedural Language/SQL or PL/SQL is the SQL dialect used 
  • One of the most expensive database solutions 
  • Works with huge databases 
  • Simple upgrades
  • Transaction control
  • Compatible with all operating systems
  • Suitable for enterprises and organizations with demanding workloads

Microsoft SQL Server

  • Commercial database developed and managed by Microsoft
  • Transact SQL, or T-SQL is the SQL dialect used 
  • Only works with Windows and Linux 
  • User-friendly
  • Difficult to make adjustments mid-process when errors are found
  • Excellent documentation
  • Works well for small-to-medium-sized organizations that want a commercial database solution without the cost of Oracle

PostgreSQL

  • Object-oriented database management system, which means it’s a hybrid SQL/NoSQL database solution 
  • Free and open-source 
  • Compatibility with a wide range of operating systems 
  • Active community and many third-party service providers 
  • High ACID compliance 
  • Uses pure SQL 
  • Works best for use cases where your data doesn’t fit in with a relational model. It also works well for extra-large databases and running complicated queries. 

NoSQL Database Systems

MongoDB is by far the most popular NoSQL database, and for good reason. Its features and benefits include:

  • Free to use 
  • Dynamic schema 
  • Horizontally scalable 
  • Excellent performance with simple queries
  • Add new columns and fields without impacting your existing rows or the application’s performance

MongoDB works best for companies that are going through rapid growth stages or those with a lot of unstructured data. Lesser-known alternatives to this NoSQL database are available, such as:

  • Apache Cassandra
  • Google Cloud BigTable
  • Apache HBase

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In summary, the five key differences between SQL vs. NoSQL are:

  1. SQL databases are relational, NoSQL databases are non-relational.
  2. SQL databases use structured query language and have a predefined schema. NoSQL databases have dynamic schemas for unstructured data.
  3. SQL databases are vertically scalable, while NoSQL databases are horizontally scalable.
  4. SQL databases are table-based, while NoSQL databases are document, key-value, graph, or wide-column stores.
  5. SQL databases are better for multi-row transactions, while NoSQL is better for unstructured data like documents or JSON.

Once you’ve decided on SQL or NoSQL databases, you need to get your data moved into them. Data migration is a complex process that may present serious challenges. If you’re running into bottlenecks in this operation, Xplenty’s Extract, Transform, Load (ETL) platform helps with automated functionality and a visual, no-code interface for data pipeline building. 

Xplenty has hundreds of built-in integrations to make it easy to work with your new database technology. Explore how simple it is to create an automated data pipeline by setting up a seven-day demo