Explore the key differences between SQL and NoSQL databases and learn which type of database is best for various use cases.
SQL is a decades-old method of accessing relational databases, and most who work with databases are familiar with it. However, as unstructured data, amounts of storage and processing power, and types of analytics have changed over the years, we’ve seen different database technologies available that are better suited to new types of use cases. These databases are commonly referred to as NoSQL.
SQL and NoSQL differ in whether they are relational (SQL
) or non-relational (NoSQL), whether their schemas are predefined or dynamic, how they scale, the type of data they include, and whether they are best suited for multi-row transactions or unstructured data.
What is a SQL database?
SQL, which stands for “Structured Query Language”, is the programming language that has been widely used in data management in relational database management systems (RDBMS) since the 1970s. In the early years, when storage was expensive, SQL databases focused on reducing data duplication.
Fast forward to today, and SQL is still widely used to query relational databases, where data is stored in rows and tables that are linked in various ways. A table record can be linked to each other or to many others, or many table records can be related to many records in another table. These relational databases, which offer fast data storage and retrieval, can handle large amounts of data and complex SQL queries.
What is a NoSQL database?
NoSQL is a
non-relational database, which means it allows different structures than a SQL database (not rows and columns) and more flexibility to use the format that best suits the data. The term “NoSQL” wasn’t coined until the early 2000s. This does not mean that systems do not use SQL, as NoSQL databases sometimes support some SQL commands. More accurately, “NoSQL” is sometimes defined as “not just SQL.”
To lay the groundwork, watch the following video by Jamil Spain:
How SQL Works
are valuable for handling structured data or data that has relationships between its variables and entities.
In general, SQL databases can scale up, which means you can increase the load on a server by migrating to a larger server that adds more CPU, RAM or SSD capacity. While scaling up is most often used, SQL databases can also scale out through partitioning or partitioning logic, although that’s not well supported.
structure SQL database schema organizes data in a relational and tabular way, using tables with columns or attributes and rows of records. Because SQL works with a strictly predefined schema, it requires organizing and structuring the data before starting with the SQL database.
RDBMS properties, which use SQL, must exhibit four properties, known by the acronym ACID. This ensures that transactions are processed correctly and that the SQL database has a high level of reliability
- Atomicity: All transactions must succeed or fail completely and cannot be left partially complete, even in the event of a system failure
- . Consistency: The database must follow rules that validate and prevent corruption at every step
- Isolation: Simultaneous transactions cannot affect each other.
- Durability: Transactions are final, and even system failure cannot “reverse” an entire transaction.
Because SQL databases have a long history now, they have huge communities and many examples of their stable online codebases. There are many experts available to support SQL and program relational data.
Examples of SQL
- databases MySQL
- Microsoft SQL Server
Azure SQL Database
NoSQL works Unlike SQL,
systems allow you to work with different data structures within a database. Because they allow a dynamic schema for unstructured data, there is less need to pre-plan and organize data, and it is easier to make modifications. NoSQL databases allow you to add new attributes and fields, as well as use varied syntax across databases.
NoSQL databases scale
out best, which means additional servers or nodes can be added as needed to increase load
databases are non-relational, so they don’t just store data in rows and tables. Instead, they generally fall into one of four types of structures:
- Column-oriented, where data is stored in cells grouped into a virtually unlimited number of columns rather than rows
- Key-value stores, which use an associative array (also known as a dictionary or map) as the data model. This model represents data as a collection of key-value pairs.
- Graph databases, which represent data in a graph that shows how different data sets relate to each other. Neo4j, RedisGraph (a graph module built into Redis), and OrientDB are examples of graph databases.
While SQL requires ACID properties, NoSQL
follows CAP theory (although some NoSQL databases, such as IBM’s DB2, MongoDB, AWS’s DynamoDB, and Apache’s CouchDB, can also integrate and follow ACID rules).
The CAP theorem says that distributed data systems allow compensation that can guarantee only two of the following three properties (which form the acronym CAP) at any given time:
- Consistency: Each request receives the most recent result or an error. MongoDB is an example of a strongly consistent system, while others like Cassandra offer eventual consistency.
- Availability: Each request does not have an error result.
- Partition tolerance: Any delay or loss between nodes does not interrupt the operation of the system.
NoSQL has been adopted quickly, it has smaller user communities and therefore less support. NoSQL users benefit from open source systems, unlike the many SQL languages that are proprietary.
When to Use SQL
vs NoSQL When using
is a good choice when working with related data. Relational databases are efficient, flexible, and easily accessible for any application. An advantage of a relational database is that when a user updates a specific record, each instance of the database is automatically updated and that information is provided in real time.
SQL and a
relational database make it easy to handle a large amount of information, scale as needed, and allow flexible access to data—they only need to refresh data once instead of changing multiple files, for example. It is also better at assessing data integrity. Since each piece of information is stored in one place, there is no problem with older versions confusing the image.
Most big tech companies use SQL, including Uber, Netflix and Airbnb. Even large companies like Google, Facebook, and Amazon, which build their own database systems, use SQL to query and analyze data.
When to use
While SQL is valued to ensure data validity, NoSQL is good when it is most important that the availability of big data is fast. It’s also a good choice when a business will need to scale due to changing requirements. NoSQL is easy to use, flexible and offers high performance.
NoSQL is also a good choice when there are large amounts of datasets (or constantly changing) or when working with flexible data models or needs that don’t fit into a relational model. When working with large amounts of unstructured data, document databases (for example, CouchDB, MongoDB, and Amazon DocumentDB) are a good choice. For quick access to a key value store without strong integrity guarantees, Redis may be the best choice. When you need a complex or flexible search on a large amount of data, Elastic Search is a good choice.
Scalability is a significant benefit of NoSQL databases. Unlike SQL, its built-in partitioning and high availability requirements enable horizontal scaling. In addition, NoSQL databases such as Cassandra, developed by Facebook, handle massive amounts of data distributed across many servers, without having single points of failure and providing maximum availability.
Other large companies that use NoSQL systems because they rely on large volumes of data not suitable for a relational database include Amazon, Google, and Netflix. In general, the more extensive the dataset, the more likely it is that NoSQL will be a better choice.
SQL, NoSQL and
Selecting or suggesting a database is a
key responsibility for most database experts, and “SQL vs. NoSQL” is a useful rubric for informed decision making. When considering any of the databases, it is also important to consider critical data needs and acceptable trade-offs that lead to meeting performance and uptime objectives.
IBM Cloud supports cloud-hosted versions of multiple SQL and NoSQL databases with its cloud-native databases. For more guidance on how to select the best option for you, see “A brief overview of the database landscape” and “How to choose a database on IBM Cloud.”
Interested in digging deeper into individual databases? Check out our “Database Deep Dives” blog post series.