NoSQL vs. SQL: What You Need to Know

February 25, 2021

There are hundreds of database options out there, but they all fall into one of two categories: NoSQL and SQL. Deciding whether you should use a SQL or NoSQL database is one of the first steps to start your search—so when it comes to the question of NoSQL vs. SQL, which is the right choice for you?

The Top 5 Most Popular SQL Databases

The DB-Engines ranking, updated monthly, collects information on the most widely used database management systems, including both SQL and NoSQL databases. As of writing, the 5 most popular SQL databases in the DB-Engines ranking are:

  • Oracle: The most popular SQL database is Oracle Database, used by more than 100,000 enterprise clients.
  • MySQL: MySQL is the most popular open-source SQL database, and has also spawned the popular offshoot MariaDB.
  • Microsoft SQL Server: Microsoft SQL Server is the tech giant’s SQL database offering and is widely used within the Microsoft ecosystem.
  • PostgreSQL: The second most popular open-source SQL database, PostgreSQL is the default database of macOS Server.
  • IBM Db2: Rounding out the top 5 most popular SQL databases is IBM Db2, which has multiple variants to support different architectures and use cases.

The Top 5 Most Popular NoSQL Databases

According to the DB-Engines ranking, the 5 most popular NoSQL databases are:

  • MongoDB: The only NoSQL database in the top 5 of the DB-Engines ranking is MongoDB, which calls itself “the most popular database for modern apps.”
  • Redis: Redis is an open-source in-memory data structure store that can be used to implement key-value NoSQL databases.
  • Cassandra: The open-source Cassandra columnar database is developed by the Apache Software Foundation.
  • DynamoDB: DynamoDB supports key-value and document databases, and is offered as part of the Amazon Web Services cloud.
  • Neo4j: Neo4j is the most popular NoSQL database that uses a graph model for storing data.

A Brief History of NoSQL

As the name suggests, NoSQL databases were born out of a desire to move away from databases using SQL (Structured Query Language), also known as relational databases. The class of NoSQL or “non-relational” databases is highly diverse, unified mainly by the fact that none of them use SQL.

The growth of NoSQL databases in the 2000s was largely in response to the rise of the Internet and new tech trends such as big data and cloud computing. As the world grew more digitized and more people and processes migrated online, NoSQL databases offered greater scalability and better performance compared with the limitations of relational databases. Below is a timeline of some of the most important developments in the history of NoSQL:

  • Late 1960s: Various predecessors to the modern NoSQL database emerge—such as MUMPS, a key-value database designed to store healthcare data.
  • 1998: The term “NoSQL” is coined by Carlo Strozzi to describe a relational database without an SQL interface.
  • 2007: Amazon publishes a paper about its Dynamo distributed NoSQL system.
  • 2009: MongoDB is released.
  • 2009: Johan Oskarsson again popularizes the term “NoSQL” for its current usage: non-relational databases.

NoSQL vs. SQL: Technical Differences

The most obvious technical difference between NoSQL and SQL is the way in which data is stored. SQL databases store data in row-column tabular format. For example, in a SQL database of students, each row represents an individual student, and each column represents some characteristic (e.g., name, ID number, address, courses, etc.). The SQL language is used to efficiently query a SQL database and find records that match the given query.

By contrast, there are four main types of NoSQL databases, depending on how they store data:

  • Document databases store data in document format (e.g., JSON or XML).
  • Key-value databases store data in key-value pairs; each pair contains a unique key that corresponds to some value.
  • Column-oriented databases organize data primarily by columns instead of by rows.
  • Graph databases store data in a graph: nodes represent data, and edges represent the relationships between the data.

NoSQL vs. SQL: Pros and Cons

While NoSQL and SQL databases both have their advantages, they also have certain flaws and limitations that you should be aware of. Below, we'll go over how SQL and NoSQL databases stack up in terms of a few important factors:

  • Standardization: NoSQL databases are only unified by the fact that they’re “not SQL,” which makes it harder to switch between different solutions. SQL databases, meanwhile, are unified by a common SQL language and relational database structure. SQL is the winner here.
  • Flexibility: Flexibility is a strength of NoSQL databases, with multiple types to fit different data formats. On the other hand, SQL databases struggle to deal with data that doesn’t fit neatly into a relational database table. That's a point for NoSQL.
  • Community support: Whereas the SQL community is large and well-established, the NoSQL community is newer and tends to be smaller and more fragmented, making it harder for platforms to expand. Another win for SQL.
  • Scalability: Scaling a SQL database becomes generally more and more difficult as the amount of data in the database grows. SQL databases generally need to “scale up” by adding more storage, RAM, or processing power. On the other hand, scalability is typically a strength of NoSQL databases (especially by using cloud computing), which can easily scale out horizontally by adding nodes and using techniques such as sharding. NoSQL comes out on top.
  • Reliability: Unlike SQL databases, NoSQL databases don’t have native support for ACID (atomicity, consistency, isolation, durability), a set of standards to improve the reliability of database transactions. SQL is better for users who need guaranteed ACID transactions for their database.

NoSQL vs. SQL: Which is Best for You?

Now that we’ve explored the question of NoSQL vs. SQL, how can you decide which type of database is right for you?

A NoSQL database is likely best if:

  • You have large quantities of unstructured data or data structured in ways other than the relational row-column format.
  • Scalability and speed are important concerns. NoSQL databases generally have a performance advantage over their SQL alternatives by not being required to support ACID principles.
  • You want to work with big data. Tools such as Hadoop have been designed to work with NoSQL databases for massively parallel processing of very large quantities of data.
  • You have a use case such as implementing a cache (which key-value NoSQL databases excel at) or storing semi-structured data (which document NoSQL databases are good for).

A SQL database is likely best if:

  • Your data neatly fits within a row-column relational schema.
  • You need to query your data frequently. Relational databases use the SQL query language for efficiently retrieving data records.
  • You want to protect the integrity of your data by following ACID principles. One common use case for SQL databases is handling financial transactions, which need to be atomic. For example, transferring money between two accounts actually consists of two transactions: decreasing the money in the first account, and increasing the money (by the same amount) in the second account. Making this operation atomic ensures that either both transactions succeed, or neither of them do—so that customers' money won't go mysteriously missing if the transaction fails partway through.

What about the process of generating synthetic data for NoSQL vs. SQL databases? The structured format of SQL databases can make them more straightforward to synthesize than NoSQL, but that's not to say it's easy. Developers face a number of challenges such as preserving referential integrity and mirroring critical relationships throughout their database—or databases. The challenges of synthesizing NoSQL's unstructured data are even greater, but progress is being made (see e.g. "Towards a Data Generation Tool for NoSQL Data Stores").

As for Tonic? We're continually adding databases to our list of integrations. Our feature-rich synthetic data generation platform currently supports 9 SQL databases and counting, and NoSQL database integrations are coming soon. Need more help with the question of NoSQL vs. SQL? We’re here for you. Get in touch with us today for a chat about your business needs and objectives, or to schedule a demo of our platform for securely generating high-quality synthetic data.


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