Knowledge of the SQL programming language is one of the most sought-after skills within the data science industry, topping multiple charts and rankings in the field. The popularity of this language directly relates to the ubiquity of SQL databases within companies and computers, because much of the information and data that we share online is stored within SQL databases and management systems. Through this understanding of SQL as a language that can be used to work with relational databases, there are several database management systems that are useful to the field of data science. The following list includes some of the top SQL databases and how they can be used by students and professionals for data science projects and portfolios.

What is a SQL Database?

A SQL database is a database that is based on relational systems and is generally compatible with the SQL programming language. Relational database systems or SQL databases are structured in such a way that you are able to compare datasets within the system. These systems are generally structured in a rows-and-column format which makes it easy to make these comparisons within the dataset and to store data within tables. As database objects, information and data can be manipulated and retrieved from these tables. In this sense, relational databases are focused on structured data which can be sorted, organized, and classified based on this system and the data types and objects within it.

While SQL is known as a programming language, as a “structured query language” the primary use of SQL is the manipulation and management of data. Much of the education around SQL includes instruction on writing queries for relational databases. Queries are used to search through a dataset, making it easier to clean and organize it. Through querying, data scientists are able to filter, sort, and group data as well as return descriptive statistics. As the primary use of SQL databases, querying can also be used as a form of data mining or exploratory analysis, which ensures that you are familiar with the dataset by examining what it contains and what might be missing.

Once data is imported into a SQL database, data scientists are also able to edit, modify, and/or update database objects. For example, SQL databases allow you to delete records or other aspects of the dataset from the database with relative ease. This is especially useful when collecting and storing data over a period of time, or when there are any changes to the metadata within a dataset or the understanding of that data. Instead of having to manually update these records (which might be a necessity if the data was stored in a file system), SQL databases allow for a greater sense of flow and transformation of information and data.

SQL Databases for Data Science

There are many database management systems that are compatible with SQL/ The following list includes some of the most popular SQL databases, with a focus on open-source data science software that can be used to clean, organize, and structure datasets.

1. PostgreSQL

Another open-source SQL database, PostgreSQL is a relational database system that is known for its high level of performance and capacity to work with large stores of data. Prioritizing security and integrity, PostgreSQL includes several features which reflect the responsiveness of this software, and the community which contributes to it, to solving some of the major challenges and concerns within database design. A versatile and scalable system, this database also offers the unique feature of not only being programmable with SQL but with a variety of programming languages, such as Python, in addition to being able to handle both structured and unstructured data.

2. Microsoft SQL Server

Included as one of many data science tools offered by Microsoft, SQL Server is well known within the data science industry and is highly compatible with Azure and Microsoft’s business intelligence (BI) products. Geared towards big data projects, this database is focused on offering speed and efficiency to data scientists that need to query large datasets. While most databases focus on the management of structured and relational datasets, SQL Server is also capable of handling multiple data types, including non-relational and unstructured data.

3. MySQL

Viewed as one of the most popular open-source SQL databases, MySQL offers several services for individuals as well as businesses as a product of Oracle database services. Students and professionals that want to receive training in MySQL can also take part in the MySQL Certification Program, which offers education for developers and database administrators. MySQL also prides itself on being the database service of choice for multiple high profile corporations and technology platforms, such as YouTube, Uber, and PayPal, therefore certification in this database system is especially useful when pursuing employment at a company that uses SQL databases.

4. SQLite

Described as a database engine, SQLite stands out from other SQL databases in that it does not have a separate server in which to store the information and data of users. Acting as a library, data scientists can use SQLite to easily migrate stores of data from one system to another because it is both compact and mobile. SQLite is generally known as a database that is used by software engineers and developers that are working with mobile applications and cellphones.

5. IBM Db2 Database

Offering several database services and programs, IBM is well respected within the world of relational database management systems. With several platforms and editions, the Db2 databases are compatible with multiple operating systems and offer services that specialize in the safety and security of information and data. As a SQL database, IBM Db2 is also a cloud-based software that makes it easy to access your data when using different computers and working in a variety of environments.

Interested in learning more about SQL Databases?

As an essential data science tool, working with SQL databases includes an understanding of how to clean, organize, and store data within a relational database management system. In addition, knowledge of SQL databases offers the potential to not only expand one’s career in data science but also database design and management. Due to the similarities between the most popular SQL databases, instruction in one can also offer a solid foundation for learning how to use one of the others, making knowledge of SQL databases a highly transferable skill.

Noble Desktop offers multiple SQL courses which not only include training in the programming language but also using SQL for working with specific databases and writing queries. The SQL Bootcamp focuses on introducing students to working with PostgreSQL and developing skills that can be transferred to databases such as MySQL. For those who want instruction in Microsoft SQL Server, the SQL Server Bootcamp is a three-day intensive that focuses on the basics of SQL. Whether you take one or both of these bootcamps, Noble Desktop’s live online courses offer high-quality instruction in a format that is accessible to data science students and professionals of all backgrounds!