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However, MySQL may still be a viable option for certain use cases. In data science, MySQL may not be the first choice due to its limited support for advanced data types and analytics features compared to other RDBMS such as PostgreSQL and MS SQL.

MySQL is a good choice for small to medium-sized web-based applications because it is easy to set up and use, and there is a large community of developers who can provide support and tutorials. It is also one of the most popular databases for use with the LAMP (Linux, Apache, MySQL, and PHP) stack. MySQL is another open-source RDBMS, which is widely used for web-based applications and is known for its reliability and ease of use. PostgreSQL supports for triggers and rules, which allows for automatic updates, data validation, and auditing of data changes in the database. PostgreSQL uses Multi-Version Concurrency Control (MVCC), which allows multiple users to access the same data simultaneously without blocking each other, while MS SQL uses a different concurrency control model that can result in more locking and blocking. PostgreSQL has connectors to many open-source ETL tools such as Talend, Apache Nifi, and Apache Airflow, which can be useful in data science projects that require ETL functionalities. PostgreSQL also supports the ACID properties, which ensures the reliability of the data and the consistency of the database. PostgreSQL is highly extensible and allows for the creation of custom functions, operators, and data types, making it a good choice for customizing the database to specific requirements. It also has built-in support for full-text search, and is often used for advanced analytics and business intelligence workloads. PostgreSQL is an open-source RDBMS with a strong support for advanced data types, such as arrays and hstore (a key-value store).

MS SQL can be easily integrated with other Microsoft technologies, such as Azure, Power BI, and Visual Studio, which can be useful for data science projects that use these tools. MS SQL also has built-in support for disaster recovery, which can be important in data science projects that require 24×7 uptime. It is a good choice for enterprise environments and for applications that require high levels of availability and scalability. MS SQL has several built-in business intelligence, data warehousing and data mining features. It is most widely used in enterprise environments and is known for its support for high-availability and scalability features. MS SQL is a proprietary RDBMS developed by Microsoft. Out of all three, MS SQL is the only one that is not open source. Even though below answers have a mingle of relational database management systems with the others, in this article, we will compare the top three RDBMS: Microsoft SQL, MySQL, and PostgreSQL.

According to the Stackoverflow community survey in 2022, the respondents were asked which database environments they have done extensive development work in over the past year, and which they want to work in over the next year.