SQL language: discover its importance in data analysis

It is used to manage information in relational databases and is an increasingly used tool by analysts who work with data sources. We are referring to the SQL language and how it facilitates the work of data scientists and related professionals. 

Data science is a rapidly growing discipline, increasingly important in the economy and for companies. It includes tools, developments, and applications that allow access to the information managed by data scientists and other professionals—resources that enable analysts to extract insights from which to make relevant strategic decisions. 

Among these solutions and resources is the so-called SQL language, the Structured Query Language, which is discussed below. It is a programming language used to manage information in a relational database that, as will be explained in this article, helps optimize the data that can be stored and processed.

What is SQL and how does it work?

What exactly is the SQL language? What does it consist of? How does it work? The Structured Query Language known as SQL is especially valued because it integrates well with different programming languages. It works based on the following principles: 

• Information is structured in collections of organized tables. 
• Tables contain data such as dates, record names, and other key references. 
• The main advantage is that they allow inserting information, updating, deleting data, and querying everything within a database.
• It is used by professionals specifically trained to manage this type of development.
• It is useful across all types of industries and sectors. 

What is SQL used for in database management?

The applications of SQL language in managing information stored in databases are varied. These provide strong reasons for Big Data specialists to master solutions like this. 

1. Data retrieval: one of its key functions, as it allows analysts to quickly and easily access stored resources. It often offers filtering by criteria and sorting results, making work easier.
2. Data manipulation: the retrieved information can be used in multiple ways. Calculations can be performed, datasets combined, or modified. It is a very versatile tool, which explains its usefulness and wide range of applications. 
3. Integration of different sources: SQL language makes it easier to combine information from different databases. This allows Big Data professionals to create more complex reports thanks to the wide variety of resources and information that can be gathered. 
4. Data summarization: analysts can create complete reports using different SQL functionalities, combining metrics and values among other elements. 
5. Data cleaning: cleaning data, removing duplicates, handling missing values, standardizing formats, etc. This “cleaning” process is essential for optimal performance of this specific programming language. 
6. Scalability of database management systems, allowing analysts to work with multiple sources efficiently and with minimal error. 
7. Optimizing data queries: reducing the time needed to access stored information and data.

Data analysts should always pursue specialization and deepen their knowledge of the Big Data field. EAE Business School Barcelona offers two highly specialized and comprehensive programs in this area, where SQL is covered among other tools and applications: the Global Master in Business Analytics and Data Strategy and the Master in Big Data & Analytics. Both are highly recommended for those who want to improve their knowledge in this field.

Imagen

SQL for data analysis: how to use SQL to extract useful information

When learning how SQL works to manage information, it is important to note that it is a broad and complex subject. The following outlines provide a general overview. In the case of data extraction—which is one of its main uses—the following steps can be followed: 

1. Identify the data sources from which you want to obtain information. 
2. Connect to the relevant database using appropriate tools. 
3. Establish a secure connection using the appropriate credentials. 
4. Write SQL queries in the chosen language, defining selection and sorting criteria. 
5. Execute the queries and verify the results to ensure everything has been done correctly. 
6. Export the data in the required format for further analysis. 

The relational databases mentioned throughout the article include examples such as MySQL, MS Access, and MS SQL Server. As previously noted, the information is structured in rows and columns with relevant details that help refine search results.

As can be seen, this is an introductory overview of SQL, since it is a complex subject that should be approached with specialized professionals. Big Data—data analytics—is a rapidly growing field, which highlights the importance of data analysts expanding their knowledge and understanding the key resources needed to perform their work efficiently.