In the world of database technology, there are two main types of databases: SQL and NoSQL — or, relational databases and non-relational databases. The difference speaks to how they’re built, the type of information they store, and how they store it.
We call them SQL and NoSQL, referring to whether or not they’re written solely in structured query language (SQL). In this article, we’ll explore what SQL is, how it makes these databases different, and how each type structures the data it holds so you can easily determine which type is right for you.
Structured Query Language (SQL) :
SQL is a programming language used by database architects to design relational databases. In an SQL database like MySQL, Sybase, Oracle, or IBM DM2, SQL executes queries, retrieves data, and edits data by updating, deleting, or creating new records. SQL is a lightweight, declarative language that does a lot of heavy lifting for the relational database, acting as a database’s version of a server-side script. One particular advantage of SQL is its simple-yet-powerful JOIN clause, which allows developers to retrieve related data stored across multiple tables with a single command.
A relational database — or, an SQL database, named for the language it’s written in, Structured Query Language (SQL) — is the more rigid, structured way of storing data, like a phone book. Developed by IBM in the 1970s.
A relational database consists of two or more tables with columns and rows. Each row represents an entry, and each column sorts a very specific type of information, like a name, address, and phone number. The relationship between tables and field types is called a schema. In a relational database, the schema must be clearly defined before any information can be added.
For a relational database to be effective, the data you’re storing in it has to be structured in a very organized way. A well-designed schema minimizes data redundancy and prevents tables from becoming out-of-sync, a critical feature for many businesses, especially those that record financial transactions. A poorly designed schema can result in organizational headaches due to its rigidity.
Another reason SQL databases remain popular is that 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.
NOSQL DATABASES: NON-RELATIONAL & DISTRIBUTED DATA
NoSQL databases, on the other hand, have dynamic schemas for unstructured data, and data is stored in many ways: They can be column-oriented, document-oriented, graph-based or organized as a KeyValue store. This flexibility means that:
- You can create documents without having to first define their structure
- Each document can have its own unique structure
- The syntax can vary from database to database, and
- You can add fields as you go.
If your data requirements aren’t clear at the outset or if you’re dealing with massive amounts of unstructured data, you may not have the luxury of developing a relational database with a clearly defined schema. Enter non-relational databases, which offer much greater flexibility than their traditional counterparts.
Think of non-relational databases more like file folders, assembling related information of all types. If a WordPress blog used a NoSQL database, each file could store data for a blog post: social likes, photos, text, metrics, links, and more.
How do NoSQL databases work? Instead of tables, NoSQL databases are document-oriented. This way, non-structured data (such as articles, photos, social media data, videos, or content within a blog post) can be stored in a single document that can be easily found but isn’t necessarily categorized into fields like a relational database does. It’s more intuitive, but note that storing data in bulk like this requires extra processing effort and more storage than highly organized SQL data. That’s why Hadoop, open-source computing and data analysis platform capable of processing huge amounts of data in the cloud, is so popular in conjunction with NoSQL database stacks.
Key Differences between SQL and NoSQL
In a relational database system, you must define a schema before adding records to a database. The schema is the structure described in a formal language supported by the database and provides a blueprint for the tables in a database and the relationships between tables of data. Within a table, you need to define constraints in terms of rows and named columns as well as the type of data that can be stored in each column.
In contrast, a document-oriented database contains documents, which are records that describe the data in the document, as well as the actual data. Documents can be as complex as you choose; you can use nested data to provide additional sub-categories of information about your object. You can also use one or more document to represent a real-world object. The following compares a conventional table with document-based objects:
In this example, we have a table that represents beers and their respective attributes: id, beer name, brewer, bottles available and so forth. As we see in this illustration, the relational model conforms to a schema with a specified number of fields which represent a specific purpose and data type. The equivalent document-based model has an individual document per beer; each document contains the same types of information for a specific beer.
In a document-oriented model, data objects are stored as documents; each document stores your data and enables you to update the data or delete it. Instead of columns with names and data types, we describe the data in the document and provide the value for that description. If we wanted to add attributes to a beer in a relational model, we would need to modify the database schema to include the additional columns and their data types. In the case of document-based data, we would add additional key-value pairs into our documents to represent the new fields.
The other characteristic of the relational database is data normalization; this means you decompose data into smaller, related tables. The figure below illustrates this:
In the relational model, data is shared across multiple tables. The advantage of this model is that there is less duplicated data in the database. If we did not separate beers and brewers into different tables and had one beer table instead, we would have repeated information about breweries for each beer produced by that brewer.
The problem with this approach is that when you change information across tables, you need to lock those tables simultaneously to ensure information changes across the table consistently. Because you also spread information across a rigid structure, it makes it more difficult to change the structure during production, and it is also difficult to distribute the data across multiple servers.
In the document-oriented database, we could choose to have two different document structures: one for beers, and one for breweries. Instead of splitting your application objects into tables and rows, you would turn them into documents. By providing a reference in the beer document to a brewery document, you create a relationship between the two entities:
In this example, we have two different beers from the Amstel brewery. We represent each beer as a separate document and reference the brewery in the brewer field. The document-oriented approach provides several upsides compared to the traditional RDBMS model. First, because the information is stored in documents, updating a schema is a matter of updating the documents for that type of object. This can be done with no system downtime. Secondly, we can distribute information across multiple servers with greater ease. Since records are contained within entire documents, it makes it easier to move, or replicate an entire object to another server.
In most situations SQL databases, they are vertically scalable, which means that you can increase the load on a single server by increasing components like RAM, SSD, or CPU. In contrast, NoSQL databases are horizontally scalable, which means that they can handle increased traffic simply by adding more servers to the database. NoSQL databases have the ability to become larger and much more powerful, making them the preferred choice for large or constantly evolving data sets.
SQL databases are vertically scalable. NoSQL databases, on the other hand, are horizontally scalable
Another major thing to consider when weighing SQL versus NoSQL is their structures. SQL databases are table-based which makes them a better option for applications that require multi-row transactions. Samples of these may be accounting systems or even legacy systems that were originally built for a relational structure. NoSQL databases can be key-value pairs, wide-column stores, graph databases, or document-based.
What database solution is right for you?
Reasons to use a SQL database
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 exactly 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. If your business is not experiencing massive growth that would require more servers and you’re only working with data that are consistent, then there may be no reason to use a system designed to support a variety of data types and high traffic volume.
Reasons to use a NoSQL database
When all of the other components of your server-side application are designed to be fast and seamless, NoSQL databases prevent data from being the bottleneck. Big data is the real NoSQL motivator here, doing things that 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 having to define what “types” of data that are 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 centres out of the box without a lot of headaches.
- Rapid development. If 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, a relational database will slow you down. NoSQL data doesn’t need to be prepped ahead of time.
Overall, the decision of using SQL versus NoSQL for business is not entirely black and white; it requires some comparing and contrasting to determine which database best fits your specific needs. With the proper amount of research and preparation, however, you will ensure that the database you choose provides an efficient and streamlined management system for your organization.
If you are in need of NoSQL today, it doesn’t mean that it will be stick on to it all day.
There might be different preferences, distinct requirements to prefer over some other database. So figure out your requirements and find out the database which wisely provides the integrated support for your project development.