SQL or Structured Query Language is the globally recognised query language for making use of all the relational databases that we have available to us. Without SQL, one will not be able to effectively perform CRUD operations and will definitely have a hard time when creating, maintaining and retrieving data from relational databases. SQL can be referred to as a key for managing relational database management systems, being used across the globe in thousands of companies.
Yes, now we can see technology like distributed file systems such as Hadoop getting adopted rapidly, however, SQL and SQL databases will always be as important as they have been for the last two decades.
Now, when talking about NoSQL databases, any database that does not have structured tables that are fixed for containing data are NoSQL databases. Fundamentally, any non-relational databases can be referred to as NoSQL databases due to them technically not being able to use SQL for their daily operations.
Let’s learn more about SQL and NoSQL databases!
What is SQL Database?
SQL databases are relational databases that follow a structured approach when storing data, thus requiring SQL in order to conduct daily functions or CRUD operations. Some of the best examples of SQL databases are Microsoft SQL Server, Microsoft Access, MariaDB, MySQL, MemSQL, PostgreSQL, SQLite, Teradata RDBMS, SAP HANA, Oracle Database, and IBM DB2. SQL is the standardised query language for all these relational databases that operate based on a structured data management platform.
When working with these RDBM systems, SQL is used by analysts, administrators and even developers for integrating data, writing scripts, and running analytical queries. With SQL, it is easy for users to modify database tables and indexes, thus making it even simpler to update, add and delete rows filled with data.
In SQL databases, most functions are carried out with easy retrieval of data subsets, thus ensuring standardised data analysis, data processing, and data transactions. In these databases, data is categorised and segregated into columns and tables, thus making it easy to identify data values or information that easily corresponds to events or other sets of data. SQL databases have been around since the 1970s and had not seen a decline in popularity till the last decade where NoSQL databases or file distribution systems such as Hadoop started gaining popularity.
However, SQL databases are as relevant as ever and most major organisations (or MNCs) and a large number of web portals (or websites) depend on SQL and SQL databases. Leaving aside unstructured and semi-structured data, SQL databases are generally the most preferred option when working with structured data and when expecting to vertically scale organisational systems or applications.
Here are some common statements used in SQL databases:
What is NoSQL Database?
Fundamentally, any database that does not have structured data and which is not relational can be referred to as a NoSQL database. Notably, NoSQL databases do not have data in the form of tables and do not follow a structured system of data storage and management. This means that these databases do not have any fixed system for extracting, storing and maintaining information or ‘values’ in datasets.
The data is stored in these databases in a non-tabular form with data types being dependent on the active data model. For instance, documents, graphs, key values and wide columns are some NoSQL data types.
NoSQL databases are more scalable than traditional RDBMS and are used for working with massive amounts of data that experience concurrent operations with multiple users. NoSQL databases are particularly useful for enormous user loads, fundamentally being useful for big data that can be accessed and effectively used by a large number of users parallelly.
Some examples of NoSQL databases are DynamoDB, MongoDB, CouchDB, Orient DB, SimpleDB, FlockDB, Cassandra, Infinite Graph, Lotus Notes, CouchBase, HBase, Riak, Neo4J and Redis. However, we must take care to not confuse NoSQL databases with Hadoop or other file distribution systems that rely on nodes or clusters.
NoSQL databases are easy to scale and integrate with modern applications, thus making them a more preferred option in recent times. It is also comparatively easier for developers to make changes and use data from NoSQL databases in real-time during development. For instance, MongoDB Atlas allows developers to map data types such as documents to objects in the programming language of their choice while modifying the schema in real-time.
NoSQL databases work hand-in-hand with applications and grow along with the project. Developers can also choose to automate infrastructural operations and optimise performance in accordance with requirements with minimal data movement in a secure manner.
There are two important types of NoSQL databases:
- Document-oriented NoSQL Databases: For example, MongoDB and CouchDB.
- Graph Databases: For example, Infine Graph and InfoGrid.
Fundamental Differences Between SQL and NoSQL Databases
One major thing that people get confused about is NoSQL databases do not literally mean ‘No SQL’ or ‘Non-SQL’. NoSQL refers to ‘Not only SQL’ and is a more flexible approach to database management compared to traditional RDBMS. Fundamentally, the main difference between SQL and NoSQL databases is the ability of NoSQL databases for storing data in other formats than just relational tables. NoSQL databases also have the ability to work with massive amounts of data that is unstructured, as compared to SQL databases being effective (or efficient) with only structured data.
However, when talking about the differences between SQL and NoSQL databases, these are not necessarily the advantages or disadvantages of these database systems. Sometimes, it is much easier to use one or the other depending on the nature of operations or the data type at hand. So, let us talk about what makes these two types of databases different.
Here are some key differences:
- SQL databases are table-based while NoSQL databases are generally document-based or graph-based. Some NoSQL databases even work on key-value pairs or wide-column stores.
- SQL databases have predefined schema while NoSQL databases are more flexible and have dynamic schema.
- SQL databases are vertically scalable while NoSQL databases are horizontally scalable.
- SQL databases are the best option when working with structured data while NoSQL databases can work with all types of data (even semi-structured and unstructured).
- SQL databases are great for complex queries with the use of SQL while NoSQL databases are not that great with queries of complex nature. NoSQL is a better fit for using attributed values or for working with a collection of documents. Meanwhile, SQL is highly suitable for extensive query operations.
- SQL databases focus on durability, consistency, isolation and atomicity while NoSQL databases focus on partition tolerance, availability and consistency.
Fundamentally, NoSQL databases either employ the use of complex data structures paired with keys or these databases attribute values to keys to form key-value pairs. Meanwhile, SQL databases simply employ collections of datasets that are segregated through records or tables and columns. NoSQL databases also support nested documents while SQL databases completely depend on tabular segregation. Now, let us get into SQL vs NoSQL pros and cons and SQL vs NoSQL performance.
Pros and Cons of SQL Databases
The main advantages of SQL databases are:
- Adjusted, standardised and affordable
- Availability of more SQL database-oriented product suites and integrations
- Existing for a longer time, thus having a larger community and more support systems behind them
- Operational atomicity
- Easy rollbacks and setups
- Compatibility when working with the right data types and information
- Efficient with complex queries and ongoing query operations
The main disadvantages of SQL databases are:
- Not efficient with large volumes of data
- Database execution can sometimes be challenging
- Complicated to use and SQL is more complex than modern document-oriented methods
- Not scalable enough, especially horizontally
- Lack of decentralisation and flexibility
- Only effective with structured data
- Requires more powerful hardware and better IT infrastructure
- Slow compared to modern NoSQL-based systems
- Not great for huge user loads or multiple concurrent operations
Pros and Cons of NoSQL Databases
The main advantages of NoSQL databases are:
- Decentralisation and adaptability
- Flexible and easy to integrate with applications or development pipelines
- More accessible and easy to use as a database
- Adjustable according to operational requirements without hampering active operations or with minimal data movement
- Enormous scalability and efficiency when working with big data or massive volumes of data
- Supports structured, unstructured and semi-structured data
- Can easily work on weaker hardware and simple IT architecture
- Supports a massive number of concurrent users
The main disadvantages of NoSQL databases are:
- Not effective with maintaining atomicity or accuracy, thus decreasing the trustworthiness of the information
- NoSQL databases are more unorganised and can face attribution problems
- Lack of standardisation
- Lack of smooth transmutation or institutionalisation across other NoSQL databases. This makes it extremely tough to change one’s setup or move to another database system
- Less support and fewer available frameworks to ensure a stable future for databases working on non-Linux systems
- Slower upgrades and almost nonexistent product suites other than the ones offered by the NoSQL database service providers.
Frequently Asked Questions
Yes, SQL is a more complex query language to use. Thus NoSQL databases are easier to use than SQL databases. Also, when talking about SQL vs NoSQL in 2021, we must agree that using SQL databases is more complex in general. However, this holistic DBMS course by Coding Ninjas will definitely help you get more comfortable with using all kinds of databases. When talking about sharding, in SQL vs. NoSQL, it is much easier to use in NoSQL as well.
NoSQL databases will never replace SQL databases but it will absolutely help many organisations who wish to use big data and unstructured data. For more information, you can also check out other uses while talking about SQL vs NoSQL in Stackoverflow.
Amazon’s DynamoDB is a NoSQL database. And, Amazon uses NoSQL-based systems for its own operations as well. This is one of the most important SQL vs NoSQL interview questions and it is recommended that you study a bit more about DynamoDB.
SQL databases have been around for a long time and have a rich history of being used by most top organisations across the world. Thus, it will be a while till NoSQL is able to truly outperform SQL in terms of popularity. However, in terms of performance and power, NoSQL is already there, especially when working with massive volumes of data and unstructured data types. Graph-oriented NoSQL databases are also becoming quite popular as compared to the traditional document-oriented NoSQL systems. For more information, you can also check out the benefits of SQL vs NoSQL vs Graph NoSQL.
No, SQL will not be dead any time soon as it is being used by major corporations and thousands of websites across the globe. It will continue to be used for a long time.
Yes, Dgraph is a NoSQL database.
MariaDB, MySQL and Microsoft SQL Server are the best examples of SQL databases.
DynamoDB, MongoDB and SimpleDB are some of the best examples of NoSQL databases.
Yes, when working with massive volumes of data, NoSQL databases are more powerful. However, SQL databases are more powerful when working with the right data types. Also, NoSQL databases are more flexible and scalable.
With changing requirements of modern times, developers are now rapidly adopting NoSQL databases and recognizing the need for scalability and flexibility. SQL will always be important and SQL databases will always be used by a huge number of organisations. However, NoSQL systems are increasingly being incorporated into application development and other development pipelines due to the minimal data movement and real-time iterations. Also, with the advent of cloud computing and the need to distribute data across several servers and areas, NoSQL is slowly becoming the more preferred option.
Data comes in all sizes and types, thus NoSQL provides the all-in-one solution to work with polymorphic, unstructured, and semi-structured data. SQL databases are great but they are the best option when working with structured data in a limited organisation environment with no need for future scaling.