What is SQL?
SQL (Structured Query Language) is a standard language used to store, retrieve, and manipulate data in relational databases.
It allows end-users to communicate with databases and perform tasks like creating, updating, and deleting databases. Almost every mid to large-sized organization uses SQL, including Facebook, Microsoft, LinkedIn, and Accenture.
In fact, SQL was ranked as the most used database environment and the third most popular programming language by StackOverflow in 2021. If you're curious about SQL vs Python, check out our separate article comparing the two. To understand what SQL is used for, let's first quickly explore what databases do.
An Introduction to Databases
Databases are collections of structured information that are stored and accessed electronically. They make it easy for companies to extract, update, and analyze internal data, often by using SQL or similar languages.
Companies use databases mainly because spreadsheets and files aren’t scalable. While they may be suitable for small companies and startups that don’t collect large amounts of data, they can only store a limited amount of information.
An Excel file, for instance, can only store approximately 1 million rows of data, and even this will slow down system performance considerably when users try to perform complex operations on it. In comparison, databases can load over 10 million rows of data in under a minute.
Furthermore, databases allow for easy data manipulation, analysis, and aggregation that simply cannot be done with a spreadsheet or text file.
There are many different types of databases used by organizations to store internal data, the most popular being relational databases.
Relational databases store data points with pre-defined relationships between them and can be accessed using a language called SQL. To get a comprehensive introduction to relational databases and how they work, check out our course.
What is SQL Used For? 7 Top SQL Uses
SQL has many uses and is used in marketing, healthcare, and finance for data and business analytics, development, and data science. In this section, we will take a closer look at what SQL is used for, along with the types of jobs that require employees to know the language.
Industries That Use SQL
Below, we've picked out some of the main areas where SQL is used to deliver a range of different outcomes.
1. SQL Uses in Marketing
Marketing teams often target customers and release promotions based on user data collected by the organization.
More often than not, this data is stored in large databases and must be queried before marketers can use it.
Here is an example of how SQL is used in the field of marketing:
Anne is a marketing executive at an ecommerce company. Every time users interact with the company’s website, their data is collected and automatically stored in a relational database. There are over 1.5 million records of user data stored in the database.
Variables collected include items purchased by the customer, their gender, time of purchase, and a unique user ID.
Here is a summarized view of the database table she needs to query:
From the table above, Anne needs to extract all the details of customers who made purchases during a sale on 25th August 2021. She wants to retarget these users for a similar marketing campaign she is running next quarter.
Here is a simple SQL query she can write to access the data she needs in less than a minute:
SELECT * FROM customer_data A WHERE CAST (A.Date AS Date) >= '2021-08-25';
This query will return the following results from the table displayed above:
The example above demonstrates just how useful SQL is in extracting relevant information analyze business data.
Since the marketing process is becoming increasingly data-driven, it is a good idea for marketers to learn basic SQL so that they can make informed decisions when running campaigns and targeting users.
2. SQL Uses in Finance
Organizations often store financial data in databases since they are highly secure and can be queried easily. Finance professionals can extract this data to make data-driven decisions.
Here is an example of how finance teams can use SQL to query relevant information from databases:
John is a finance specialist who analyzes quarterly sales data to develop forecasts for the next year. He also needs to identify periods with an exceptionally low number of sales to prevent this from happening again.
Here is a sample table called “sales_data” that John needs to work with:
This is a simple SQL query he can write to identify months with lower-than-average sales:
SELECT Month_of_Year, Sales FROM sales_data WHERE Sales < (SELECT AVG(Sales) FROM sales_data);
He can use the results from this query to identify patterns in sales and use it to create a financial plan for the upcoming year.
Apart from the use case displayed above, finance teams can also use SQL to identify trends in revenue and sales over time. The language can be used to provide decision-makers with a clear view of financial KPIs such as the company’s net income and costs.
3. SQL Uses in Healthcare
SQL is often used to manipulate and analyze patient information stored in clinical databases. For example, it can be used to build dashboards on user health data, generate patient reports based on input from medical professionals, and even create searches against large databases like the Covid-19 tracker.
Here is an example of how SQL is used in the healthcare domain:
A non-profit charitable organization collects data related to a deadly disease that is prevalent in rural areas. Volunteers then visit high-risk areas with the largest number of infections to administer vaccines to curb the spread of the disease.
Here are five rows of a sample database created by the organization:
This is an SQL query that can be written to identify the top 10 regions with the highest number of infections:
SELECT * from diseases ORDER BY Cases DESC LIMIT 10
While the query above is useful, it is inefficient for volunteers to run it on a daily basis to find high-risk regions. Instead, the SQL database can be connected to a live, real-time dashboard that visualizes the number of infections in different regions.
Here is a sample Covid-19 dashboard that does just that:
The use cases above show us that SQL has applications in many domains. It is used in almost every industry to analyze data and drive business gain.
Due to this, it is worthwhile for non-technical professionals to learn basic SQL so they can interact with databases and collect the necessary information to make everyday decisions.
How SQL is Used in Specific Jobs
Now that we know what SQL is used for in various industries, let's look at some specific jobs that utilize its powers.
4. SQL For Mobile and Web Development
Developers use SQL when building mobile and web applications. When you install a mobile app and create a username and password, these are immediately translated into a code that cannot be deciphered by attackers and stored securely in a database using SQL.
Similarly, when you interact with the application, subscribe to a newsletter, or make a purchase online, your data is collected and stored in a database.
Back-end mobile and app developers need to be fluent in SQL to perform data processing tasks and create a pleasant user experience.
5. SQL For Data Analytics
Data analysts are skilled professionals who identify trends in data to aid in the company’s decision-making process. They use SQL for data extraction and analysis to generate actionable insights.
Managers and stakeholders use the patterns uncovered by data analysts to make decisions that add business value to the organization.
Data analysts are some of the most sought-after professionals in the world. Take the Data Analyst in SQL career track to begin your transition into the field today.
6. SQL For Business Analytics
Business analysts identify areas of improvement within the organization and generate recommendations to strengthen business processes.
They can work in different company areas, such as IT or staff development, and need to recommend changes in these divisions based on available data.
This means that business analysts do not only have to collect and analyze data using SQL, but they also need to create interactive dashboards to present their findings to upper management.
SQL can query databases connected in real-time with visualization tools such as Tableau and PowerBI, making it an ideal language for business analysts to use.
If you want to become a business analyst or already are one and want to improve your SQL skills, check out our SQL for Business Analysts skill track.
7. SQL For Data Science
Data science involves extracting insights from data, and SQL is key for this, especially in predictive modeling and analysis. SQL is crucial for:
- Data Extraction and Preprocessing: Vital for querying and preparing data from databases, enabling filtering, sorting, and aggregation for high-quality data sets.
- Exploratory Data Analysis (EDA): Helps data scientists understand patterns and correlations in databases, aiding in hypothesis formation and modeling approaches.
- Feature Engineering: Used to create new variables from existing data to enhance model performance, utilizing SQL's functions and table joining capabilities.
- Data Wrangling for Machine Learning: Transforms and structures data for machine learning, dealing with normalization, missing values, and categorical encoding.
- Integration with Analytical Tools: Seamlessly integrates with tools and languages like Python and R, combining SQL's data manipulation with advanced analytics.
- Scalability and Performance: Handles large data volumes efficiently, crucial for time and resource-limited environments.
- Reporting and Visualization: Generates data sets for visualization tools and reporting software, ensuring accurate and up-to-date reports.
- Real-time Data Science: Quick querying and processing abilities are vital for real-time applications like dynamic pricing, fraud detection, and recommendation systems.
Why Should You Learn SQL for Data Science?
When most people think of data science, their minds often jump to predictive analytics and machine learning modeling. However, you can only build machine learning models if you have the data necessary to do so. In the real world, this data will rarely be handed to you in a clean, structured spreadsheet like it is on Kaggle.
You need to access the company’s database, collect the data you need, and pre-process it before you can start building predictive models. As mentioned above, most companies store data in relational databases, meaning you need to know SQL to achieve this.
In fact, according to a recent study, SQL is a requirement in almost 65% of all data science job listings. This means that even if you know Python, you are missing out on around 3 out of 5 job opportunities if you lack SQL skills.
If you still aren’t convinced, here is Meta’s data scientist job description that lists SQL as a must-have skill:
Read our article to learn about what you can do with SQL if you master the language.
How to Learn SQL for Data Science
If you have no prior SQL knowledge, take our Introduction to SQL course to learn about the fundamentals of the language. This program will teach you about data organization, how tables are structured in databases, and the principles of database construction. You will learn to select data from databases and perform basic data manipulation.
Once you’ve learned the basics of SQL, take the Intermediate SQL course to learn data analysis with SQL. This course covers concepts like data filtering, sorting, grouping, and aggregate functions. These operations will teach you to uncover insights and identify trends in large amounts of data.
Then, take the Joining Data in SQL course to learn how to combine two or more database tables. This is arguably one of the most important types of SQL query that is used by data scientists on a daily basis.
Next, take Data Manipulation in SQL to learn more advanced SQL functions such as subqueries and window functions.
The above courses should give you a strong grasp of SQL. The concepts learned in these courses will prepare you for SQL data science interview questions.
However, to really become proficient at a language and use it in the real world, you need to apply the knowledge you gained.
Take the Applying SQL to Real World Problems program to learn to solve business problems using SQL. The queries you write when taking the course can be included in your data science portfolio and will increase your chances of landing a job in the field.
SQL is not just a programming language, but a crucial tool across various industries. Its ability to efficiently manage and analyze large data sets makes it indispensable in our increasingly data-driven world. Whether in marketing, finance, healthcare, or technology, SQL's versatility enables professionals to unlock the potential of data, leading to informed decisions and innovative solutions.
For anyone navigating the digital landscape, learning SQL is not just an enhancement of their technical skills but a necessary step towards future-proofing their career. As data continues to drive business and technology forward, SQL remains a key skill for harnessing the power of information in any field. Take our SQL Fundamentals skill track today and learn how to write SQL queries and start analyzing your data.
SQL Uses FAQ
Is SQL a Programming Language?
Yes, SQL is a programming language since it is Turing complete. In simple words, a language that is Turing complete should be able to solve any computational problem given enough time and memory.
However, unlike programming languages like Java and Python, SQL can only interact with databases. It cannot be used to visualize data, read files, and build web applications. Due to this, SQL is called a domain-specific programming language.
Read our article to learn more about whether SQL is a programming language.
How to Ace Data Science SQL Interviews?
Data science interviews typically assess candidates on topics such as joins, finding duplicates in a database table, index functions, window functions, grouping, and subqueries. All these concepts are covered in Datacamp’s SQL Fundamentals career track.
Most companies will provide you with a sample dataset and a business problem. You then need to use a combination of SQL functions to write a query that solves the use case at hand. To practice writing queries that solve real-world problems, take one of Datacamp’s guided SQL projects.
Can I Use SQL to Build Machine Learning Models?
Yes, it is possible to build machine learning models with SQL. Google Cloud Platform (GCP), a popular cloud computing service, has recently released a service that allows users to run SQL queries in Google BigQuery.
BigQuery is a serverless data warehouse used by organizations to store and manage large amounts of data. Data in BigQuery is stored in the form of structured, relational databases.
BigQuery ML allows users to build machine learning models directly in BigQuery using SQL. It supports algorithms like linear regression, logistic regression, XGBoost, K-Means clustering, ARIMA, and neural networks.
To learn more about cloud computing services like GCP and how they work, take the Understanding Cloud Computing course on Datacamp.
Top SQL Courses
10 Portfolio-Ready SQL Projects for All Levels
SQL vs NoSQL Databases: Key Differences and Practical Insights
MySQL Basics Cheat Sheet
PostgreSQL Basics Cheat Sheet
MySQL Tutorial: A Comprehensive Guide for Beginners
SQL Server Tutorial: Unlock the Power of Data Management