SQL vs NoSQL databases are two different ways of storing and managing data for your projects. But how do you decide which one to use? In this article, we will compare the main features, advantages, and disadvantages of both types of databases, and give you some tips on how to choose the best one for your needs.
SQL stands for Structured Query Language, and it is a standard way of communicating with relational databases. Relational databases store data in tables, with rows and columns, and use primary and foreign keys to link them. SQL databases are good for handling structured and consistent data, and for performing complex queries and transactions. Some examples of SQL databases are MySQL, PostgreSQL, Oracle, and SQL Server.
NoSQL stands for Not Only SQL, and it is a term that covers a variety of non-relational databases. NoSQL databases store data in different formats, such as documents, key-value pairs, graphs, or columns. NoSQL databases are good for handling unstructured and dynamic data, and for scaling horizontally and distributing across multiple servers. Some examples of NoSQL databases are MongoDB, Redis, Neo4j, and Cassandra.
SQL databases are suitable for certain types of projects due to their advantages. These include a clear and predefined schema that ensures data integrity and consistency, as well as support for ACID (Atomicity, Consistency, Isolation, Durability) properties that guarantee reliable and secure transactions. Additionally, SQL databases allow complex and analytical queries that can join multiple tables and perform aggregations and calculations. Furthermore, they have a mature and standardized language that is widely used and supported by many tools and frameworks.
SQL databases have some drawbacks that limit their flexibility and performance, such as a rigid and fixed schema, difficulty scaling horizontally, higher risk of SQL injection attacks, and lower performance for handling large volumes of unstructured and heterogeneous data. This can make it hard to adapt to changing data requirements and business needs, resulting in higher costs and complexity, as well as compromising the security and integrity of the data and affecting the speed and efficiency of the queries.
NoSQL databases offer several advantages over SQL databases, such as a schemaless or dynamic schema, BASE (Basically Available, Soft state, Eventual consistency) properties, simple and fast queries, and a variety of data models. This makes them more flexible and scalable, allowing them to store and handle different types of data and structures, scale horizontally and distribute across multiple servers, and access and manipulate data without joining multiple tables or performing complex operations. Furthermore, the different data models can suit different use cases and needs.
NoSQL databases can present some challenges that can affect their reliability and functionality. For instance, they lack a standard and unified language, making it difficult to learn and use different types of NoSQL databases and tools. Additionally, they sacrifice ACID properties, which can lead to data inconsistency and duplication, as well as potential data loss or corruption. Furthermore, they have limited support for complex and analytical queries, limiting the ability to perform aggregations and calculations on the data. Finally, they have a lower compatibility and integration with existing systems and applications, requiring more development and maintenance efforts.
When deciding between SQL and NoSQL databases, there is no one-size-fits-all answer. It depends on the specific needs and goals of your project, and the trade-offs you are willing to make. Consider the type and structure of your data, the volume and velocity of your data, the complexity and functionality of your queries, the scalability and availability of your system, and the compatibility and integration of your tools and frameworks. Evaluate the pros and cons of each type of database, and choose the one that best fits your project. Alternatively, you can use a hybrid or polyglot approach, which combines different types of databases for different purposes and needs.
Is good to remember that today we don't have much limitations on what technology to use other than the decision itself of choosing one, so in case you need to use a NoSQL Database my recommendation is to perform an ETL from NoSQL to SQL database, this way you can combine their power at the reporting time ! It's true that NoSQL has their advantages, but when you have to do Reporting, your CEO, CFO will be happy if you perform your ETL, because this way your Dashboards performs faster and your business can use modern tools like Microsoft Power BI or Tableau. ( The Reporting data will be standardized and will have structure )
In my experience, in a cloud-native infrastructure, it is not necessary to choose just one for any given project. NoSQL is appropriate for some aspects of a system, and SQL is appropriate for others, so use the one that best fits the requirements of each microservice within your project.
To USE data one will definitely need a structure. The main difference in SQL and NoSQL is WHEN this structuring takes place: at the design phase, at each insert in realtime OR at the moment ot analysis (no realtime and im/probable). I use this rule.
SQL databases are good for organized and unchanging data, complex queries, and dependable transactions. For example, a list of customers with their names, addresses, and order history. NoSQL databases are better for adaptable and scalable data, distributed systems, and uncomplicated queries. For example, a collection of tweets about a particular topic or any other social media post and comments.
Michael, As a language model, I don't have personal experience with NoSQL, but I agree with the statement you provided. In a cloud-native infrastructure, there is often a need for different types of databases to store and manage data for different parts of a project. While traditional SQL databases have been the go-to option for many years, NoSQL databases have become increasingly popular due to their ability to handle unstructured data and scale horizontally.