SQL vs. NoSQL: The Most Important Differences
SQL is a query language created in the 1970s as a way for users to communicate with relational databases to store, organize, and manage data. SQL is popular worldwide with companies of all sizes that need to store and analyze data.
NoSQL refers to a newer type of database (called a non-relational database) that doesn’t require you to use SQL.
While relational databases (sometimes called SQL databases) have been the primary model for data management over the past decade, non-relational databases are gaining popularity as an alternative model for managing data. They are particularly well suited for companies that need to store and maintain large amounts of unstructured data.
Let’s discuss the differences between SQL and NoSQL, how NoSQL’s design helped overcome some challenges of SQL, and why this evolution in database management is happening.
What is SQL?
SQL (pronounced “sequel”) stands for Structured Query Language. Developed in the 1970s, SQL is the industry-standard language for organizing, editing, and managing relational databases.
SQL databases are table-based. This means they organize and store data in tables with predefined categories or columns. Relational databases contain structured data, such as names, email addresses, and phone numbers. A relational database matches data by using common characteristics found in the dataset, resulting in a group called a schema.
Using SQL, you can add, delete, search, update, and organize data records in a relational database. It has since become an industry standard, with many popular relational databases using SQL, including MySQL Database, Oracle, and Microsoft SQL Server.
Using SQL vs. using Excel
One reason SQL became popular is that it provided a more efficient way to do data analysis than Microsoft Excel.
Many companies have switched from using Excel for data analysis to SQL, which can perform the same tasks and report generation quicker and easier. Excel provides a record-keeping system with tables and columns. However, you still have to conduct data analysis directly in the Excel worksheet using methods like building a Pivot Table to relate records to each other or writing if-then statements to generate reports. You can conduct a similar analysis in Google Sheets with formulas like VLookup.
By switching to storing their records in a relational database rather than a spreadsheet program, organizations can use SQL as the query language to manipulate, relate, and analyze those records. This allows them to handle larger amounts of data and generate reports and analysis much faster.
Many professionals who already know how to do advanced data analysis in Excel or Google Sheets can pick up skills in SQL quickly. Both rely on the same underlying knowledge of how to manipulate data records in tables to organize, compare, and generate reports to help draw meaningful conclusions from the data.
There are many databases that use SQL. They are all pretty similar, but each uses a slightly different version of the SQL language. Here are some common SQL databases with a few details about them:
- Free and open-source
- Available for all major platforms
- Huge community of developers
- Also has a procedural language
- Advanced transaction control
- Free and open-source
- High ACID compliance
- Has JSON fields for unstructured data
- Microsoft SQL Server
- Commercial database system by Microsoft
- Also has a procedural language for writing procedures
- Works on Windows and Linux
What is NoSQL?
NoSQL (pronounced “no sequel”) stands for Not Only SQL, or Non-SQL, and refers to non-relational databases. While SQL itself is a query language that communicates with databases, NoSQL is an adjective used to describe a non-relational database that doesn’t require the SQL language.
NoSQL databases emerged in the late 1990s to handle big data that could have differing structures — an area where relational databases fell short. In a NoSQL database, you can store unstructured data across multiple servers and processing nodes. Because non-relational databases don’t require fixed table schemas, they can not only store structured data but also semi-structured and unstructured data. This makes them easier to scale and manage, especially for organizations with massive data storage needs.
Some examples of NoSQL databases include MongoDB, RavenDB, Cassandra, BigTable, and CouchDB.
Most NoSQL databases can contain the following four data types:
- Document-oriented stores. These let a key pair with a document. Documents can hold a variety of objects like key-array and key-value pairs, as well as other documents.
- Key-value stores. These are simple databases that store information in the form of attributes (keys) and values. In some cases, values can have types like “string” or “integer.”
- Graph stores. These store data about networks in a graph-oriented format.
- Wide-column stores. These help with handling large amounts of data in the form of columns.
With auto-sharing, NoSQL databases can share data across servers without complex programming or code. This balances the processing load of storing and managing data across multiple servers, whereas SQL databases rely on a single server. This also makes NoSQL databases more secure in the event of a server crash — if one server goes down, the others in the system will still function and be able to access and store data.
Modern programming is often iterative, meaning that programmers gradually add to a database or application over time. An iterative programming approach can be a challenge when using SQL servers because of their fixed schema structure. This makes it time-consuming to add to or change the structure of the data and records in a SQL database.
NoSQL databases are more flexible and compatible with iterative programming. Because defining schemas isn’t necessary at the start and NoSQL databases can handle more than one type of data, programmers can add or change the structure of data and records as they go. These minor changes don’t disrupt the entire system or require large data transfers.
All SQL databases are pretty similar to each other. But the similarity between NoSQL databases often stops at not using SQL. There are quite a few types of NoSQL databases, with MongoDB being the most common. Here are some common NoSQL databases:
- Stores data as JSON documents
- Can scale quickly
- Is really fast for simple queries
- Stores key and value sets
- Is very fast
- Is commonly used for high-speed caching
- Provided by Amazon Web Services
- Similar to MongoDB
- Data stored as JSON documents
- Can be queried from a web browser
- Built-in conflict resolution
What are the key differences between SQL and NoSQL?
There is an intense debate between developers regarding the merits of each system, even making its way to Google I/O 2012 in a debate titled SQL vs NoSQL: Battle of the Backends. Each has its own unique strengths and weaknesses, but some experts predict that NoSQL may eventually replace SQL.
So, just what are these differences? Let’s take a look at some of the key features separating SQL and NoSQL.
SQL databases are relational databases (RDBMS), meaning they store data in tables with predefined columns and rely on fixed table schemas to relate data records to each other.
NoSQL databases are non-relational (or distributed) databases, meaning they do not require tables. Non-relational databases can store unstructured data using documents, key-values, graphs, or columns.
Fixed schema vs. dynamic schema
SQL databases require a fixed (or predefined) schema. This means you must define the structure of your data first before working with it. All data must follow that same structure. This requires a lot of preparation upfront. Changing the structure (or schema) of your data is difficult and time-consuming, as it requires starting over with a new predefined schema and update all your records to match the new schema. Even small changes may require system downtime or reduced service for a period while the database gets updated.
NoSQL databases use dynamic schemas, meaning you can create data without having a defined structure in place. Each data record can have a unique structure, typically organized as a document, graph, column, or key-value. This gives you far more flexibility, as you don’t have to spend time upfront defining a schema, and you can change and update the schema as you go. Change management is far easier and rarely requires any system downtime for small adjustments in records and structure.
Scalability — vertical vs. horizontal scaling
SQL databases scale vertically. This means that the database uses a single server. To scale (or increase the load) on that server, you can add RAM, add or upgrade the CPU, and add or upgrade the SSD. The only way to scale a SQL database for increased data is to improve the server’s capacity or purchase a larger, more expensive server that can handle more data storage.
NoSQL databases are horizontally scalable. They rely on nodes that can share data and processing power. This means that you can add more servers to a NoSQL database to handle more data (increase the load). NoSQL databases can use multiple servers and share data across them.
Because they use low-cost hardware, it’s often less expensive to scale NoSQL databases, and it requires little or no application downtime. This makes it easier to scale as the amount of data increases and means that NoSQL databases can become far larger than SQL databases, which are limited to only one server.
Data volume capacity
While SQL databases can store millions of records, they do hit a storage limit eventually. One server can only hold so much data before it hits its maximum capacity, no matter how large the server. Purchasing increasingly larger servers also brings steep financial commitment, meaning most companies will hit a maximum server size that they can afford to use for a relational database.
On the other hand, NoSQL databases can handle a far higher volume of data, as they can spread data across thousands of connected servers. For organizations that need to store massive amounts of unstructured data, NoSQL databases are the obvious choice. They can handle far more data than SQL databases, with almost no impact on application performance.
SQL databases need a separate infrastructure to cache data, which requires additional hardware and slows the performance of the databases. NoSQL databases can cache data directly within the system memory, resulting in much higher performance.
Advantages of NoSQL databases
Non-relational (NoSQL) databases have features that work better with many modern applications. Large organizations now collect massive amounts of data — much of it unstructured — and the amount of data stored increases every day.
For large organizations, the relationships and tables that make up SQL databases can number in the millions. Combine this with millions of users performing lookups in these tables, and the system can suffer major performance issues. This is one of the main reasons organizations like Google and Amazon have switched to non-relational database systems.
In addition, large-scale programming projects using complex data types and hierarchies, such as XML and JSON, are difficult to incorporate into SQL. These data types, which can contain objects, lists, and other data types themselves, do not map well to tables consisting of only rows and columns.
Maintaining high-end relational database systems is expensive and is only possible with the assistance of highly skilled database administrators. This requires a significant investment for organizations.
On the other hand, NoSQL databases require less management overall. Features like automatic repair, easier data distribution, and simpler data models mean that NoSQL databases require less administration and tuning than SQL databases.
NoSQL databases typically use clusters of cheap commodity servers to manage increasing data and transaction volumes, while relational databases tend to rely on expensive proprietary servers and storage systems. So, in general, storing and processing data costs less in a NoSQL database than it does in a SQL database.
Here is a breakdown of when you want to use a NoSQL database:
- You need more than a traditional RDBMS model can provide.
- You need a flexible data schema.
- You are logging data from distributed sources.
- You don’t need the constraints and validation logic a SQL database provides.
Advantages of SQL databases
Though NoSQL databases have generated a lot of enthusiasm, there are several obstacles they must overcome to appeal to more mainstream companies. Some NoSQL solutions are still in the pre-production stages, and they have yet to implement many features developers look for. So for many applications, a SQL database may be the better choice.
Customer support is currently stronger for relational databases because they have been around longer and have a global community of users. Relational database vendors also provide a higher level of enterprise support.
Because NoSQL databases are newer, small start-up companies provide NoSQL system support. Although NoSQL is growing, the current providers lack the global reach, resources, or credibility of Oracle, Microsoft, or IBM — the big names associated with SQL.
While NoSQL databases have evolved to meet the scaling demands of modern Web 2.0 applications, their query languages are harder to use for ad-hoc query and analysis.
It is much easier to code a SQL query, which makes ad-hoc analysis a simple task. In NoSQL, even a simple query requires significant programming expertise. Commonly used BI tools don’t provide connectivity to NoSQL. This means NoSQL has a steeper learning curve, especially for users accustomed to working with Excel and SQL.
Currently, the complexity of NoSQL makes it a good fit primarily for large technology companies that collect massive amounts of unstructured data and can afford to pay a trained database administrator with knowledge of NoSQL.
Smaller projects and organizations typically function just fine with SQL. While it may be difficult in some cases, it’s possible to map complex objects using tables. There are powerful tools available, such as the Oracle database system, which are very effective.
Here is a breakdown of when you want to use a SQL database:
- You want a simple query language to interact with your data.
- You want to use joins and complex queries.
- Your data is strictly structured and that structure will not change often.
- Your business rules depend on transactions.
SQL and NoSQL Examples
To get an idea of the difference between handling data in a SQL database and a NoSQL database, let’s look at an example using e-commerce orders.
If you are using a SQL database, you have to plan your tables before you use it. All of the data will go into columns and rows. In other words, it will be flat. So you will need a customers table to hold information about your customers. But since your customer may have different shipping and payment addresses, you will also have to create an address table to hold these, so that one customer record can reference more than one address record.
Here is an example of the the queries you will have to write in order to create the tables you need in a SQL database:
CREATE TABLE customers(id integer, name string);
CREATE TABLE customer_address(id integer, customer_id integer, address string, city string, state string, zip string);
Then the query that you would write to retrieve a customer with all that customer’s addresses would be:
SELECT * from customer JOIN customer_address ON customer.id = customer_address.customer_id;
In a NoSQL database like MongoDB, you can store both the customer and their addresses in the same record and you don’t have to create the tables beforehand, just a collection to hold your records. So that record can look like this and can be inserted directly into the database.
name: "Bob Smith",
address: '555 First St.',
city: 'St. Louis',
address: '101 Main St',
NoSQL makes inserting records quick and easy, while SQL allows for more control over your data structure.
How to choose between SQL and NoSQL
So, how do you decide whether a SQL database or a NoSQL database is right for your data needs? The choice often comes down to a few key factors.
What type of data are you using?
First, consider the type of data you’re working with. For structured data, SQL is likely the right choice. SQL databases work well for transaction-based needs, such as customer relationship management (CRM) solutions, e-commerce software, and accounting tools. A relational database will store this data in a structured way, with each row representing a distinct record and each column a distinct attribute in that data.
On the other hand, if you are working with primarily unstructured data that won’t fit neatly into a table format or you aren’t sure of the nature of the data you’ll be handling, a NoSQL database would be a better option. SQL databases cannot handle unstructured data and are less flexible when changing the structure of data in the middle of a project.
What are your compliance needs?
Second, consider your compliance needs. If you need to comply with ACID (Atomicity, Consistency, Isolation, and Durability), a SQL database is best. Because relational databases store records in a fixed table schema with distinct relationships between rows and columns, they are well suited to ACID compliance.
If you don’t necessarily require your data to comply with the ACID model, and you need more flexibility, a NoSQL database may be a better fit. NoSQL databases follow the BASE model (Basic Availability, Soft state, and Eventual consistency). This makes the database more flexible and scalable and allows you to change your data structure mid-project with little or no downtime for your application.
How will you query your data?
Third, consider how often you’ll need to query your data. It’s far easier to write queries with SQL than with a NoSQL query language. Querying a NoSQL database requires writing complex queries. If you will need to query your data quickly or the user responsible for querying is not an advanced programmer, SQL is a better fit. If you won’t need to query your data often or can afford the skill set of a NoSQL data analyst, then you may prefer a NoSQL database.
What are your future data needs?
Finally, consider how your organization expects to scale moving forward. If you will need to handle exponentially larger volumes of data, a NoSQL database is easier and less expensive to scale up. SQL databases are more costly to scale and often require system downtime during the data migration process.
SQL and NoSQL have both been revolutionary inventions in the data management field. They have made massive improvements in data storage and retrieval.
It’s hard to say one is better than the other. Both technologies are considered the best in what they do, and it is up to a developer to evaluate their business needs before choosing a database. Though NoSQL databases are becoming an important part of the database landscape, enterprises should proceed with caution and be aware of the limitations associated with this technology.
Nonetheless, it’s worth keeping an eye on NoSQL technology. We can expect to see more advancements in NoSQL database technology that will make it more usable for all types of data needs. SQL and NoSQL systems can both perform well when managed by competent administrators. The best choice will depend on how people plan to use the database and their skill-level.
If you’re in the middle of preparing for a SQL interview, check out these 20 interview questions that will help you land the job.
Frequently Asked Questions
Is SQL faster than NoSQL?
SQL databases can be faster than NoSQL databases when it comes to joins, queries, and updates. For reading and writing large amounts of unstructured data, NoSQL is faster than SQL.
Is SQL safer than NoSQL?
Because SQL databases abide by ACID properties that guarantee reliable data transactions, SQL is safer in this context than NoSQL. SQL databases also used role-based security down to the row and column level.
Is MySQL different from NoSQL?
Yes, they are different. MySQL is a specific type of relational database management system that uses SQL as its query language. NoSQL is a term that applies to many types of non-relational databases that don’t use the SQL query language.
Top courses in SQL
SQL students also learn
Empower your team. Lead the industry.
Get a subscription to a library of online courses and digital learning tools for your organization with Udemy Business.