For years, SQL solutions have been the de-facto standard for most database needs, however over the past few years NoSQL has emerged as a versatile alternative designed to address the needs of developers involved with complex data projects. While NoSQL has emerged as one of the hottest technologies in the industry, SQL systems still play a vital role as they are designed to handle different types of projects. Read more about migrating NoSQL to databases and its issues.

The Definitions of SQL and NoSQL

Structured Query Language (SQL)

Since its introduction in the early 1970’s SQL has revolutionized the way applications handle stores of information. The most important feature of SQL is that it is structured, meaning that it is suited for information which can be placed into categories and organized. Common examples of SQL in practice include storing personal information, product specification data and anything where the format of your information is predictable.

The key aspect is that with SQL, most of the data is stored in columns and must conform to the schema you originally specified. This works fine when storing names, SKU’s, addresses and other standardized information, however in today’s era of web applications, SQL’s rigid structure makes it impractical for many types of applications. One of the biggest limitations of using structured information is that as you need to scale your server, you need to keep adding on additional powerful hardware which have the processing power needed to process complex queries to make sense of your data.

NoSQL – A Modern Take on Databases

NoSQL has recently gained traction because it allows it is a database system which does not adhere to a relational database management system (RDMS) structure. Rather than relying on tables and using queries to manipulate data. It also may not provide full ACID (atomicity, consistency, isolation, durability)~guarantees, but still has~a distributed and fault tolerant architecture.

Rather than relying on joins to combine information, the NoSQL taxonomy supports key-value stores, document store, BigTable, and graph databases, all of which make joins unnecessary.

One of the biggest advantages of NoSQL technologies is that the lack of a rigid structure means that you can use cheaper hardware clusters rather than needing high end hardware across the board.

Common NoSQL Engines

Cassandra

Cassandra is a NoSQL database platform developed by the Apache Software Foundation which is the creator of the Apache Web Server, which is the leading server suite on the internet. Known as an “almost SQL” database, Cassandra’s language CQL3 is very similar to SQL however the biggest difference is that to ensure scalability there are no joins or aggregate functions.

Key features of this platform include:

• Out of the box integration with Apache Hadoop – a leading framework for distributed computing.
• Ability to access and deliver data in near real-time regardless of the load.
• A column based architecture which is more rigid than document based databases however this is useful for complex datasets.
• Built-in geographical distribution support ensures that you can optimize content delivery based on user location.

The best applications of Cassandra are when projects that involve web analytics to count hits by hour, by browser, by IP, etc. Transaction logging and data collection from huge sensor arrays are also common applications of such systems. Common use examples of Cassandra involve projects where data is so large it cannot be stored on a single server but you still need a familiar interface to manage it.

CouchBase

CouchBase server is a form of NoSQL technology which is optimized for interactive applications. In particular the database is designed to service many concurrent users; creating, storing, retrieving, aggregating, manipulating and presenting data. Whether it is a large scale server cluster or a single instance, CouchBase can handle virtually any size network.

The biggest features of this platform include:

• Simple node management allows you to configure clusters with a few clicks. Auto-sharding ensures that loads are efficiently distributed across your infrastructure. The ability to scale across geographies and serve data from the locations which are closest to your users.
• Constant high performance by setting a constant read/write latency regardless of operation type.
• Continuous uptime even when updating software and performing hardware maintinance. If a full datacenter failure were to occur, you can easily restore your data from a standby cluster through the replication feature.
• Flexible data models by using a schemaless JSON-based data model which is much easier to work with than traditional structured platforms. Different structures can be used for different data types so the performance is always maximized.

The best use case for CouchBase is for any application where low-latency data access, high concurrency support and high availability is a requirement. Common use cases include low-latency use-cases like ad targeting or highly-concurrent web apps like online gaming (e.g. Zynga).

MongoDB

MongoDB is one of the biggest players in the NoSQL space because of its ease of use regardless of whether it is used for a single application or a distributed platform involving thousands of nodes.

Key features of this platform include:

• GridFS – Allows users to store files of any size without complications to the database. GridFS however is technically not a file system.
• It is more performance oriented than many other NoSQL solutions by focusing on being lean rather than feature packed.
• MongoDB maintains some properties of SQL making the platform easier to pick up than other platforms.
• A simple update process ensures that you can focus on coding without having to worry about updates and maintenance bringing down your server.

MongoDB is best suited for when you need dynamic queries; if you prefer to define indexes, not map/reduce functions and if you need good performance on a big database. If you were looking to implement MySQL or PostgreSQL but predefined columns held you back, Mongo is the solution for you.

Conclusion

When it comes to choosing the right database system for your project, i.e., SQL vs NoSQL, it is important to note that SQL and NoSQL both have strengths and weaknesses which make them suited for different purposes. The previously mentioned information is only intended to be a primer on the differences of the most common NoSQL solutions. Every project has different needs which is why you need to ensure that before choosing a platform, you do the research to ensure that it is a good fit for all your needs.