The names of the two main types of databases, SQL and NoSQL, refer 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?
- What is a NoSQL database?
- Common types of NoSQL databases
- Main differences between SQL and NoSQL
- SQL database examples
- NoSQL database examples
- SQL and NoSQL in Stackoverflow
- When to use SQL and NoSQL
- Which is better: SQL or NoSQL?
What is an SQL database?
An SQL database—also known as a relational database—is 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 SQL 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 that retrieves 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 of the greater flexibility they offer.
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.
Common types of NoSQL databases
There are four common types of NoSQL databases:
This least complex NoSQL option stores data in a schema-less way that consists of indexed keys and values. Examples: Cassandra, Azure, LevelDB, and Riak.
Wide-column store arranges data tables as columns rather than rows. It’s more than just an inverted table—its columns allow for excellent scalability and high performance. Examples: HBase, BigTable, HyperTable.
Taking the key-value concept and adding more complexity, each document in this type of database has its own unique structure, and a special key is used to retrieve data. It’s a great option for storing, retrieving, and managing data that’s document-oriented but still semi-structured. Examples: MongoDB, CouchDB.
Graph databases are used for interconnected data best represented as a graph. This method is capable of lots of complexity. Examples: Polyglot, Neo4J.
Main 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.
Data structure and 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.
- NoSQL. NoSQL models make sense for less structured data sets because they don't rely on a predefined model to analyze the data.
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. A popular way to horizontally scale your database is known as sharding, or splitting up your database into components on multiple servers.
When it comes to querying your data, SQL databases can execute more complex queries than NoSQL databases. How you run queries depends on the database in question.
- SQL. SQL systems have straightforward data queries because they rely on one language. The language is easier to pick up for less tech-savvy team members.
- 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 knowledgeable in information technology to pull data queries.
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 the key-value pairs model, column storage, document database, and graph database.
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
Popularity and ease of use largely depends on the type of data you will be dealing with.
- SQL. SQL databases are best for complex queries.
- NoSQL. NoSQL databases are the best option for 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 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. It’s a document-oriented database with JSON-like documents in dynamic schemas used on the back end of sites like Craigslist, eBay, and Foursquare. It’s open-source and free with good customer service.
- 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.
- Apache HBase. Another Apache project developed as a part of Hadoop, this open-source 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.
- Firebase. Powered by Google, Firebase is a NoSQL database that allows developers to save, update, and retrieve data in real-time. It’s cross-platform, providing a data storage solution to mobile, web, and cloud applications.
SQL and NoSQL in StackOverflow
StackOverflow is a popular platform for sharing technical knowledge. It receives about 100 million monthly visitors, and more than 21 million questions have been raised on the platform since its launch. StackOverflow is an example of an application housing both SQL and NoSQL databases.
StackOverflow’s SQL database is responsible for storing core and structured data such as questions, answers, comments, votes, and user information. The database runs on the Microsoft SQL server capable of scaling according to user demand. Information can be stored, updated, and retrieved quickly from the SQL database, allowing StackOverflow to support the needs of its huge user base. The SQL database also offers multiple security features, including encryption and authentication, which keeps user data safe.
StackOverflow also uses Redis, a NoSQL database, for caching, allowing users to quickly access specific information. Caching improves application performance and saves costs since it eliminates the need for a constant connection to the SQL database. StackOverflow can cache API calls, session storage, database queries, and other complex computations.
When to use SQL and NoSQL
Apart from their unique features, the choice on whether to use SQL and NOSQL databases could boil down to your project requirements. We help you determine when to use SQL and NOSQL in the following sections.
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 data integrity by prescribing how transactions interact with the database. In line with ACID compliance, SQL databases support normalization out of the box, helping developers boost data quality by identifying and eliminating repetitive records. 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 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.
Which is better: SQL or NoSQL?
The answer to which is better between SQL and NoSQL databases depends on your project requirements and use cases. Both SQL and NoSQL databases have their strengths and weaknesses. Here are some of the factors that can help you determine which database is right for your project.
- Flexibility. SQL databases use a predefined schema or data model, making them quite rigid. For example, you have to change the whole database structure in case you wish to add new types of data. On the other hand, NoSQL databases are more dynamic and can accommodate ever-changing data without any major overhaul. So, if you value flexibility, then NoSQL databases are suitable for you.
- Scalability. SQL databases scale vertically and thus require more RAM and CPU to accommodate extra load. NoSQL databases scale horizontally, meaning you can add more servers to your existing stack for extra functionality. NoSQL databases are better at scaling than SQL databases, which facilitates high availability.
- Performance. SQL databases are faster when it comes to performing queries requiring joins. In this context, joins allow developers to fetch data that match specific details from different tables. NoSQL databases are better for retrieving large amounts of data or updating records in real-time.
- Query language. SQL databases use standard SQL syntax for queries, making it easy for developers to work with databases from different vendors. NoSQL databases have varying syntax or programming languages, meaning they’re more difficult to adopt.
- ACID conformance. SQL databases use predefined schemas, meaning they are more consistent and reliable. On the other hand, NoSQL databases focus more on flexibility and speed and less on ACID standards. If you prefer consistency, atomicity, and durability, SQL databases are the better option—but when it comes to speed, NoSQL databases have an edge.
- Cost. SQL databases consume more resources, especially CPU, RAM, and SSD as they scale, which makes them more expensive to maintain than NoSQL databases. If you’re dealing with lots of data, consider NoSQL databases—but research their different pricing options and choose one that suits you.
- Community. SQL databases have a wide community of users, making it easy to collaborate and learn from peers—while NOSQL databases have smaller and more focused groups. If you wish to be part of a large community, SQL databases are a better option.
Navigate the data landscape with Upwork
There are many ways to use both SQL and NoSQL databases, and one of the biggest factors in deciding which makes the most sense for you 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 overtime. For large amounts of data with little structure, consider a NoSQL database. We can connect you with top database talent to help you understand your options.
If you’re a database expert looking for work, start your search on Upwork. With SQL and NoSQL database projects being posted regularly, you can find a job that’s right for you and earn extra income. Get started today!
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