The popularity of the SQL programming language has resulted in increased investment in SQL-based relational database management systems. As the collection of information and data becomes more complex, these systems are valued for their ability to collect, organize, and manage collections of different types of data and larger datasets. At the same time, each SQL database offers its own unique features that make them useful for specific data science projects. Known for its integration with multiple products and programming languages, Microsoft SQL Server is one of the top names in the industry. The following post focuses on some of the many reasons why Microsoft SQL Server is one of the most widely used SQL databases and a tool that every data scientist should know.
What is Microsoft SQL Server?
Created in the late 1980s, Microsoft SQL Server is a relational database management system (RDBM) that is known for its longevity and integration with various tools and software. SQL Server is also compatible with several programming languages, such as R, Python, and Ruby. Due to this fact, Microsoft SQL Server employs data science packages and libraries from within and outside of the Microsoft family, such as Spark and Apache Hadoop. This compatibility with various languages and libraries is also reflected in one of the most unique aspects of Microsoft SQL Server which is its use of the T-SQL dialect. T-SQL is the language used to write queries in SQL Server, and while it is very similar to SQL, there are a few differences between the two when it comes to keywords and the sequence of commands.
In addition to working with Windows, SQL Server works with other operating systems and platforms that are popular amongst information technologists and programmers, such as Linux. While SQL Server is currently in its 2019 edition, the latest version of the software (SQL Server 2022) is soon to be available and includes features such as greater integration of Microsoft Azure, faster processing speeds, and increased focus on compliance with security protocols. For developers and data professionals alike, Microsoft SQL Server is easily one of the most widely used data science tools for database management and design. Consequently, SQL Server is well recognized by data science professionals and the 2021 Stack Overflow Survey ranks Microsoft SQL Server as the fifth most popular database management tool.
Microsoft SQL Server for Data Science
Microsoft SQL Server has many uses and features which make it one of the most popular relational database management systems on the market. For data scientists, SQL Server is especially useful for creating a data warehouse and managing a database, its inclusion in the Microsoft family of SQL databases and data science tools, as well as its ability to keep sensitive data and personal information safe and secure.
The Microsoft Family of Data Science Tools
One of the greatest benefits of working with SQL Server is the fact that it is a part of a large ecosystem of data science tools that are produced by Microsoft. SQL Server is included in a larger portfolio of Microsoft Azure cloud databases, which makes it easier to work with a dataset that needs to be migrated from one system to another. These Azure database services include SQL Server on Azure Virtual Machines, Managed Instance, SQL Database, and SQL Edge.
SQL Server is also compatible with Microsoft’s business intelligence (BI) tools, such as Microsoft Power BI and other analytical tools. Through this integration with other products, data scientists that already use Microsoft software for other aspects of data analysis or visualization would benefit greatly from learning how to use SQL Server to store their data. In addition, learning SQL Server opens up an opportunity to learn more about these other data science tools.
Data Warehousing and Database Design
For data scientists that work within larger corporations or businesses, data warehousing is an especially important aspect of database management and design. Data warehousing is the horizontal clustering of multiple databases, and regardless of the size of the organization that you are a part of, the Microsoft Azure SQL Data Warehouse can be used to store large amounts of data across different nodes.
Each node represents one specific database, and these nodes are connected to each other, allowing for greater storage capabilities within the data warehouse. In this sense, data warehousing allows you to reduce the cost of data storage in an organization and increase the speed and efficiency of the system. Especially as parallel processing and other forms of horizontal scalability become more common within the field, data scientists should learn to use this feature when using SQL Server.
Safety and Security of Sensitive Data
Another shift within the computer and data science industries is greater consideration of cybersecurity when it comes to the storage of information and data. With data privacy being one of the top concerns of the 21st century, the safety and security of frequently exchanged data, as well as sensitive data such as personally identifiable information (PII), is on the minds of every software and technology company. Consequently, data scientists that are working with sensitive data need to ensure that the database management systems that they use are both safe and secure.
For multiple years, SQL Server has been ranked as a secure database by the National Institute of Standards and Technology’s (NIST) and the database is committed to complying with multiple safety protocols and standards of data storage. Data scientists that use SQL Server can be assured that the information stored in this database management system is being protected through layers of encryption, passwords, and the ability to monitor and edit who has access to the database.
Interested in learning SQL Server?
With knowledge of the SQL programming language becoming more common amongst data scientists, Microsoft SQL Server is one of many database management systems that has gained popularity within the industry. For those that want to know more about SQL Server, Noble Desktop offers multiple bootcamps which include instruction on this highly ranked database system. For example, the SQL Server Bootcamp incorporates instruction on Microsoft tools for database management with a focus on SQL Server.
For students and professionals that want an introduction to the SQL programming language in conjunction with relational databases, Noble Desktop also offers SQL Courses in a live online format that prioritize instruction on foundational skills in the field. Offering SQL classes for students of all levels, SQL Level 1 is an excellent course for beginners that want to learn more about the SQL programming language as well as SQL Server. For students who are more advanced in their knowledge, classes such as SQL Level II and SQL Level III offer even more in-depth instruction and ensure that you have the foundation to become a SQL professional!