There are two main types of databases: SQL and NoSQL—referring to whether or not they’re written solely in Structured Query Language (SQL).
This article will explore what SQL is, how it makes these databases different, and how you can easily determine which type is right for you.
What is an SQL database?
An SQL database—also known as a relational database—and named for the programming language it’s written in, Structured Query Language (SQL). It’s the more rigid, structured way of storing data. A relational database management system (RDBMS) executes queries, retrieves data, and edits data by updating, deleting, or creating new records. SQL is a lightweight, declarative language that does the heavy lifting for the relational database, acting as a database’s version of a server-side script.
SQL databases remain popular because they fit naturally into many venerable software stacks, including LAMP and Ruby-based stacks. These databases are well understood and widely supported, which can be a major advantage if you run into problems. One particular advantage of SQL is its simple-yet-powerful JOIN clause, allowing developers to retrieve related data stored across multiple tables with a single command.
What is a NoSQL database?
NoSQL databases–also known as “non SQL” or “not only SQL,” store data in a format other than relational tables. If your data requirements aren’t clear at the outset or if you’re dealing with massive amounts of unstructured data, you’ll want to use non-relational databases because they offer greater flexibility.
How do NoSQL databases work? Instead of tables, NoSQL databases are document-oriented. They’re more like file folders, assembling related information of all types but not necessarily categorizing the data.
Note that NoSQL databases offer more flexibility, which makes them more intuitive. NoSQL databases come in a variety of types based on their data model. The main types are document, key-value, wide-column, and graph.
What are the common types of NoSQL databases?
There are four common types of NoSQL databases, specifically:
- Key-value store model—the least complex NoSQL option, which stores data in a schema-less way that consists of indexed keys and values. Examples: Cassandra, Azure, LevelDB, and Riak.
- Column store—wide-column store stores data tables as columns rather than rows. It’s more than just an inverted table—sectioning out columns allows for excellent scalability and high performance. Examples: HBase, BigTable, HyperTable.
- Document database—taking the key-value concept and adding more complexity, each document in this type of database has its data and unique key used to retrieve the data. It’s a great option for storing, retrieving, and managing data that’s document-oriented but still semi-structured. Examples: MongoDB, CouchDB.
- Graph database—have data that is interconnected and best represented as a graph. This method is capable of lots of complexity. Examples: Polyglot, Neo4J.
What are the differences between SQL and NoSQL?
When deciding whether or not to use an SQL and NoSQL database, you’ll want to understand some of the following key differences before deciding.
Data Structure/Models
Remember that a key difference between SQL and NoSQL databases is how the data is structured. SQL databases are table-based, while NoSQL databases are non-relational. This means that a SQL database contains structured data and can perform an analysis using a relational model. A NoSQL database contains unstructured data, which means it cannot be analyzed through predefined models.
SQL
SQL or relational models work better with structured data. This is because structured data is easy to search and analyze, much like data that is laid out in a table with columns and rows.
NoSQL
NoSQL models make sense for less structured data sets because it doesn’t rely on a predefined model to analyze the data.
Scaling
Consider scaling your database when you begin to see a large increase in the amount of data stored. You can either scale horizontally by adding additional servers to your database or vertically by increasing the storage size of your existing servers. However, how you scale will be dependent on whether you’re using an SQL or NoSQL database.
SQL
Most SQL databases are vertically scalable, meaning that you add more RAM or CPU to your existing single server to increase storage space.
NoSQL
NoSQL databases are horizontally scalable, meaning that you simply add more servers to your database to get more storage space. Because of the ability to add additional servers, NoSQL databases offer a much more distributed database. A popular way to horizontally scale your database is known as sharding. Sharding refers to the concept of splitting up your database into multiple components to sit on multiple servers.
Query Data
When it comes to querying your data, SQL databases tend to be able to execute more complex queries than NoSQL databases, but whichever data set you’re using, how you run queries depends on the database in question.
SQL
SQL systems are straightforward when it comes to data queries because they rely on one language. Because it’s been around for a long time, a lot has also gone unchanged, making the language easier to pick up for team members who might be less tech-savvy.
NoSQL
NoSQL systems are a bit more complex when it comes to running queries. Because there is no set query language, your team will likely need to work with a developer or someone a bit more knowledgeable in information technology to pull data queries.
Data Storage
In regards to data storage, SQL is more straightforward, while NoSQL offers more complex data storage solutions.
SQL
The SQL storage model is a table with fixed rows and columns, which programmers refer to as a relational database.
NoSQL
There are multiple storage models for NoSQL databases, including key-value model, column storage, document database, and graph database.
Performance
For performance, programmers developed NoSQL databases to solve some of the performance issues facing SQL databases–particularly when it comes to scalability.
SQL
If you run into performance issues with your SQL database, you’ll likely need a larger server to accommodate the increasing amount of data. But, a larger server isn’t always the most cost-effective way to manage your data.
NoSQL
Instead of purchasing larger servers with more memory, NoSQL databases can scale horizontally, meaning you can add new servers to what you already have as needed.
Popularity & Ease of use
Finally, this largely depends on the type of data you will be dealing with when it comes to popularity and ease of use.
SQL
SQL databases tend to be best for complex queries.
NoSQL
NoSQL databases tend to be the best option when you’re using unstructured data.
SQL database examples
Below are some popular SQL databases or RDBMSs:
- Oracle—an object-relational DBMS written in the C++ language. If you have the budget, this is a full-service option with great customer service and reliability. Oracle has also released an Oracle NoSQL database.
- MySQL—the most popular open-source database, excellent for CMS sites and blogs.
- Google Cloud SQL—a fully managed relational database service for MySQL, PostgreSQL, and SQL Server offered through Google.
- Sybase—a relational model database server product for businesses primarily used on the Unix OS, the first enterprise-level DBMS for Linux.
- Amazon RDS—Amazon RDS for SQL Server makes it easy to set up, operate, and scale SQL Server deployments in the cloud.
- Microsoft Azure—a cloud computing platform that supports any operating system while letting you store, compute, and scale data in one place. A recent survey even put it ahead of Amazon Web Services and Google Cloud Storage for corporate data storage.
- MariaDB—An enhanced, drop-in version of MySQL.
- MS SQL Server—a Microsoft-developed RDBMS for enterprise-level databases that supports both SQL and NoSQL architectures.
- PostgreSQL—an enterprise-level, object-relational DBMS that uses procedural languages like Perl and Python, in addition to SQL-level code.
NoSQL database examples
Below are some of the popular NoSQL databases:
- MongoDB—the most popular NoSQL system, especially among startups. A document-oriented database with JSON-like documents in dynamic schemas instead of relational tables is used on the back end of sites like Craigslist, eBay, and Foursquare. It’s open-source, so it’s free, with good customer service.
- Apache’s CouchDB—is a true DB for the web. It uses the JSON data exchange format to store its documents; JavaScript for indexing, combining, and transforming documents; and HTTP for its API.
- Redis—a popular key-value database.
- Riak—an open-source key-value store database written in Erlang. It has fault-tolerance replication and automatic data distribution built-in for excellent performance.
- HBase—another Apache project, developed as a part of Hadoop, this open-source, non-relational “column store” NoSQL DB is written in Java and provides BigTable-like capabilities.
- Oracle NoSQL—Oracle’s entry into the NoSQL category.
- Apache’s Cassandra DB—born at Facebook, Cassandra is a distributed database that’s great at handling massive amounts of structured data. Anticipating a growing application? Cassandra is excellent at scaling up. Examples: Instagram, Comcast, Apple, and Spotify.
When to use NoSQL and SQL
When to use SQL
When it comes to database technology, there’s no one-size-fits-all solution. That’s why many businesses rely on both relational and nonrelational databases for different tasks. Even as NoSQL databases gain popularity for their speed and scalability, there are still situations where a highly structured SQL database may be preferable. Here are a few reasons you might choose an SQL database:
- You need to ensure ACID compliance (Atomicity, Consistency, Isolation, Durability). ACID compliance reduces anomalies and protects the integrity of your database by prescribing how transactions interact with the database. Generally, NoSQL databases sacrifice ACID compliance for flexibility and processing speed, but for many e-commerce and financial applications, an ACID-compliant database remains the preferred option.
- Your data is structured and unchanging. Suppose your business is not experiencing massive growth that would require more servers, and you’re only working with consistent data. In that case, there may be no reason to use a system designed to support various data types and high traffic volume.
When to use NoSQL
When all of the other components of your server-side application follow a design that allows them to be fast and seamless, NoSQL databases prevent data from being the bottleneck. Big data is the real NoSQL motivator here, doing things traditional relational databases cannot. It’s driving the popularity of NoSQL databases like MongoDB, CouchDB, Cassandra, and HBase.
- Storing large volumes of data that often have little to no structure. A NoSQL database sets no limits on the types of data you can store together and allows you to add different new types as your needs change. With document-based databases, you can store data in one place without defining the “types” of data in advance.
- Making the most of cloud computing and storage. Cloud-based storage is an excellent cost-saving solution but requires data to be easily spread across multiple servers to scale up. Using commodity (affordable, smaller) hardware on-site or in the cloud saves you the hassle of additional software, and NoSQL databases like Cassandra are designed to be scaled across multiple data centers out of the box without a lot of headaches.
- Rapid development. Suppose you’re developing within two-week Agile sprints, cranking out quick iterations, or needing to make frequent updates to the data structure without a lot of downtime between versions. In that case, a relational database will slow you down. NoSQL data doesn’t require preparation ahead of time.
Conclusion
There are many different ways to use both SQL and NoSQL databases, and one of the biggest factors in deciding what database makes the most sense for you to use largely depends on what kind of data you’re storing. You’ll want to use a SQL database if you’re dealing with structured data that won’t change much over time. If you’re dealing with a large amount of data with very little structure, you’ll want to consider a NoSQL database. We can connect you with top database talent to help you understand your options.
