For data scientists who are looking to expand into database management, there are a multitude of database management tools and systems from which to choose. These systems fall under multiple categories, with SQL and NoSQL databases being the primary method of classifying and understanding database management systems. While there are many ways that SQL and NoSQL databases are differentiated from each other, many companies have worked to create databases that can be used for multiple formats and types of information and data. In creating more all-inclusive databases, it can be more difficult for data science professionals to choose between different systems. The following article focuses on two of the most popular database management systems, PostgreSQL and MongoDB, which are representative of the differences between SQL and NoSQL databases, as well as the similarities between the two.
PostgreSQL vs. MongoDB for Data Science
Acting as examples of some of the most popular databases on the market, PostgreSQL and MongoDB are representative of SQL and NoSQL databases respectively. As a SQL relational database management system, PostgreSQL is an open-source database that is known for its architecture and data security. Whereas MongoDB is a NoSQL document database that is commonly used within the worlds of data science and web development. One of the primary similarities between PostgreSQL and MongoDB is the ability to work with document data regardless of database designation. This is especially unique because one of the defining differences between SQL and NoSQL databases is the ability to work, or not work, with unstructured data.
While NoSQL databases are known for their compatibility with unstructured data, SQL databases are understood as working with structured data only. However, not all SQL database management systems are confined to the collection and analysis of structured data. Databases such as PostgreSQL allow users to work with unstructured data, such as document-based data, through the incorporation of text-based data interchange formats like JSON. Data scientists who are interested in popular NoSQL databases, like MongoDB should consider whether their needs can be met by using a SQL database that is compatible with document data, like PostgreSQL.
When to Use PostgreSQL: Data Analytics and SQL Databases
Despite the fact that data scientists can use PostgreSQL for some forms of unstructured data, it is still widely known as a SQL relational database management system. Therefore, most data scientists will use PostgreSQL to work with structured datasets that rely on the rows and columns format that is typical of relational database management systems. In this sense, the benefit that PostgreSQL has over MongoDB is its ability to engage in the analytics common within SQL databases, such as advanced querying.
Additionally, PostgreSQL supports other complex data types like arrays, network addresses, and even geospatial data. PostgreSQL can be used when building a SQL database that is required to store sensitive user data, such as personally identifiable information, or even data for mapping projects. As an open-source relational database management system, PostgreSQL also has a community of data scientists and developers that contribute useful resources to the blog and other online communities and forums. This makes working with the database instructive to beginners who need more guidance when working with relational database management systems.
According to the 2021 Stack Overflow Developer Survey, PostgreSQL is also the second most popular database amongst data scientists and developers, making it a staple at many large companies that choose to incorporate open-source software. These companies include but are not limited to Apple, Instagram, and the movie database IMDB. By offering complex data types, PostgreSQL is the go-to SQL database for data scientists that are working with user data and recommendation systems. However, the lack of horizontal scalability means that PostgreSQL is not always the best option for big data projects.
When to Use MongoDB: Data Science and Mobile Development
As the fourth most popular database according to Stack Overflow, MongoDB is the only NoSQL database ranked in the top five database management systems for data scientists and developers. Known as a document-based NoSQL database, many of MongoDB’s features are also geared towards the collection of sensitive data, with a focus on online transactions and user data in the format BSON. BSON is the format that MongoDB uses to store documents, and it includes multiple data types such as strings, timestamps, and other data.
Due to its capabilities as a cloud-based NoSQL database management system, MongoDB is highly scalable and allows ease of movement of large stores of data across platforms and servers. This makes the database useful within industries where data mobility is important i.e. sharing data from one place to another. MongoDB is a popular database for data scientists within the finance, healthcare, and retail industries, where it is important to keep records on patients and consumers. The database does a good job of collecting and storing data on interactions and experience, while also being capable of analyzing user engagement.
In addition to its designation as a NoSQL database, MongoDB is also very useful for web developers and software engineers. MongoDB is commonly employed in the creation of mobile applications and games, as well as being known for its compatibility with the agile development of software and technology. This makes MongoDB a stand-out platform for both data scientists and engineers, with the MongoDB Realm platform being used to work on mobile application development both online and offline.
Need to learn more about SQL vs. NoSQL Databases?
Despite their differences, many data scientists and database managers are interested in learning both SQL and NoSQL databases. Offering multiple classes and bootcamps, Noble Desktop gives students and professionals the opportunity to take part in a variety of SQL and NoSQL courses. Noble Desktop offers SQL courses that are focused on both the SQL programming language and relational database management systems. For those interested in PostgreSQL, the SQL Bootcamp focuses on learning how to write queries in this SQL database management system. In addition, students and professionals that want to know more about MongoDB can take the NoSQL Databases with MongoDB course in order to work with JavaScript and learn more about how to develop mobile applications and database models.