Data is now considered the world’s most valuable resource.
To store, manage, and understand the valuable data we collect, tech professionals have to choose the right type of database and data structure.
Over the years, databases have fallen into two camps: SQL (relational) and NoSQL (non-relational).
For beginners, it can be intimidating to learn about database structure. Databases have decades of development and it seems daunting to try to understand the nuances of language, structure, scaling options, and more.
The Development of SQL and NoSQL: A little history
SQL was developed in the 1970s to help grow the computer industry. Prior to SQL, computer science relied heavily on mathematics and specialized computer programming. Computer scientists used complex query languages that only trained specialists could implement.
Scientists realized there was a need for a more understandable and accessible language. Hence, SQL was born.
SQL –Structured Query Language– became the shared language among different relational databases. Relational systems, such as System R, Ingres, Oracle, and MySQL, all used this standardized language to encourage consistency and accessibility for computer professionals.
Then, the Internet happened.
In the 1990s the emergence of the web began an explosion of data volumes that continues today. These huge data volumes, and new kinds of non-structured data, proved too much for the relational databases model to handle. This led directly to the development of non-SQL databases that are better equipped to handle large data volumes and unstructured data such as documents and images.
Google and Amazon created their own non-relational solutions (MapReduce, Bigtable, and Dynamo) to solve the need for more flexible and expandable data storage capabilities. These systems paved the way for NoSQL to dominate the data storage industry.
NoSQL systems or Not Only SQL systems became popular because they are more scalable and offer higher performance, and can handle very large volumes of unstructured data.
It did not take long before computer programmers came face to face with the challenges of NoSQL. NoSQL databases all have unique query languages which cause many inefficiencies. Not only are there more languages to learn for professionals, but it requires more work for connecting databases to applications and developing new and unique tools for each company.
NoSQL databases also have data consistency and security issues that have long been solved in the relational world largely due to the maturity of the SQL model.
A Return to SQL
Faced with the challenges of NoSQL, the industry swung back to the tried and tested SQL, but with modern improvements. NewSQL databases fully embrace SQL while addressing the scalability and performance needs NoSQL databases were created for.
New SQL databases remove the need to learn new complex languages, while also providing the NoSQL benefits of scalability and performance.
SQL vs NoSQL: What’s the Big Difference?
The fundamental difference between SQL and NoSQL is that they are different languages. SQL databases typically use only one structured query language. NoSQL databases use unique and non-universal languages.
SQL is the most standardized data storage language. This can be very useful because it creates consistency and accessibility. The drawback is that SQL can be restrictive in its standardized nature because all data must follow the same structure.
NoSQL has the advantage of being unstructured, creating more opportunities to store and process non-conforming data. Professionals can create data without defining their structure first. The syntax is less limiting, and fields can be added on the fly. The drawback is that database professionals have to learn a whole new language and coding for applications is much more complex.
SQL databases are mostly vertically scalable. This means that adding power to the database takes place by reinforcing the infrastructure. This can look like increasing RAM, CPU, or SSD on a single server. Scaling databases vertically is more limited because you can only add on to what you already have, rather than creating completely new infrastructure.
NoSQL databases are horizontally scalable. This means that instead of improving infrastructure, companies use sharding and adding servers to increase data storage capabilities. Sharding takes big data sets and separates them into smaller pieces, or shards. These smaller shards can be spread out across different servers. This can allow for larger and more flexible data.
SQL databases are called relational databases because these storage systems use tables and relational algebra to process data. This is often a good fit for applications that have predetermined rows with a relational structure, like accounting applications.
Schema has to be predetermined and structured. Changing the database would come at a large cost. SQL is well-designed for complex queries, but not hierarchical.
NoSQL databases are non-relational. These include document databases, key-value stores, wide-column stores, and graph stores. This structure allows for a more flexible model. Companies can store different types of structured or unstructured data.
It allows for agile teams to update their schema as they need to without having to work backward and create downtime for applications. NoSQL databases are often preferred for storing hierarchical data and large data sets.
TrustRadius has detailed reviews of products in both the relational database (SQL) and NoSQL Database categories. Product reviews are an excellent source of unbiased intelligence from actual users and are an important tool in the product selection process.