What is SQL for Data Analysis: Uses, Tools, and Queries
Want to turn a messy spreadsheet into meaningful insights in seconds? That is the potential of SQL for data analysis. Today, with the abundance of data, there are not enough data professionals who understand SQL – especially if you are considering a role as an SQL business analyst. But why is SQL so important in data analysis? It is because it is fast, powerful, and the foundation of data-driven decision-making. The possibilities of SQL in data analysis are endless, from filtering through millions of rows to enabling instantaneous summaries. Once you learn and apply advanced SQL functions for data analysis, you are not just manipulating numbers; you are building visual stories with data.
Now, are you ready to become a master of the one tool that every top analyst can not seem to live without? Let’s get started!
Table Of Content
What is SQL?
What is SQL for Data Analysis?
Why SQL is Essential for Data Analysts in 2025
How the Use of SQL in Data Analysis Drives Smarter Decisions
Must-Have SQL Tools for Effective Data Analysis
Frequently Used SQL Queries in Data Analytics
Unlock Your Career Potential with Jaro Education
Conclusion
Frequently Asked Questions
What is SQL?
SQL, which stands for Structured Query Language, is a standard programming language for managing and manipulating relational databases. Relational databases store data in tables that can be related to one another by using certain keys or identifiers to generate the relationships between the tables. SQL provides the ability to create, read, update, and delete records in a database. SQL is a popular query language because it is flexible, easy to learn and understand for performing common functions and powerful for high-precision management of large data sets.
What is SQL for Data Analysis?
SQL (Structured Query Language) is a powerful tool utilized by data analysts for extracting, transforming, and analyzing data from relational databases. SQL is used by analysts to write targeted queries to extract targeted datasets—an essential component to understand patterns and trends.
Key Functions of SQL within the analysis:
Data Retrieval: Analysts use SQL to run tailored queries to extract only the data required from vast stores of large databases; they focus on pertinent data.
Data Cleaning and Transformation: SQL has functions to filter, format, join, and reshape the data cleanly and determine that the data is accurate and ready to analyze.
Data Exploration: SQL allows analysts to drill down into attributes/data, to explore data distributions, trends, and outliers.
In summary, SQL is the crux of the analysis, allowing professionals to manage and understand data smoothly and accurately.
Why SQL is Essential for Data Analysts in 2025
For data professionals, SQL for data analysis is now an essential skill. In 2025, data-driven decisions will permeate every industry. Data professionals will have to depend on SQL (structured query language) to extract, filter, and subsequently manage larger and larger amounts of data stored in databases. SQL for data analysis represents just one of the use cases for SQL. What does SQL allow us to do with data analysis? Aside from very quickly accessing raw data to find answers, like identifying customer trends or answering whether sales performance met targets, data professionals (business analysts, data scientists, etc.) will need to use SQL to create custom reports and summaries—ultimately creating better and smarter decisions. The main reason we use SQL for data analysis is that it is a structured approach that is both powerful and easy to learn. SQL contains numerous functions that analysts can employ for data analysis, including:
COUNT, SUM, and GROUP BY; these and many other functions offer data professionals a structured way to quickly identify data patterns, gain insights, and assess. Put simply, learning SQL gives analysts the power to perform a multitude of complex data tasks easily and inexpensively. For each yearly data analysis period beyond 2025, SQL for data analysis will be indispensable!
How the Use of SQL in Data Analysis Drives Smarter Decisions
1. Dealing with Dirty Data
In educational settings, data is typically presented in a clean, organized, and readily usable format, facilitating effective learning and practice. However, in real-world analytical roles, a significant portion of an analyst’s time is dedicated to handling “dirty” or messy data. This type of data is characterized by being incomplete, incorrectly formatted, or duplicated, rendering it unsuitable for direct analysis. Common issues include missing, extra, or inconsistent values.
For example, a dataset might contain duplicate entries where unique values are expected, or crucial columns might have null values. Inconsistent values could mean that the same product is listed under different names. When encountering null values in a professional setting, I would investigate the cause: was the data entered incorrectly, or was it simply unavailable? My subsequent action would depend on the reason, ranging from manually filling in the missing data to simply documenting the reason for its absence.
2. Understanding Relationships Between Tables
Ideally, data will have an easy-to-read ERD (entity relationship diagram) showing how the data is related to one another, but the reality is the ERD might not exist, or it could be so complex that you couldn’t understand it in the first place. To write meaningful queries, you need to understand the relationships between data tables. For example, knowing how a ‘customers’ table relates to an ‘orders’ table can be the difference between an accurate query and a bad query.
If available, please begin by consulting your company documentation. The documentation can be a useful resource to understand table relationships. I also like to write the basic table relationships on a piece of paper as a way of mapping them out. However, in general, this will take time, but it’s a foundational aspect of your work as a data analyst.
3. Crafting Queries
In a course, there usually is one correct answer. In the real world, SQL is as much an art form as it is a skill. Writing queries is ambiguous, and there is not necessarily one single “correct” way to write queries and get the information you need. Eventually, you hone in on a style and methodology that works for you, along with your preferred tools and techniques to extract data quickly.
For example, if you want to know the total sales amount for a certain product category from an e-commerce database, you’d be taking data from two tables—orders and products. The orders table contains the sales data, while the products table contains the various product categories.
Must-Have SQL Tools for Effective Data Analysis
SQL provides a range of tools and features that are essential for data analysis. These tools are integrated into the SQL language and are available through various SQL database management systems (DBMS). Here are some of the key tools and features in SQL for data analysis:
- SELECT and FROM Statement
The SELECT statement is the foundational tool for SQL for data analysis. It allows you to retrieve specific data from a database table, including specific columns, rows, or calculated values. The FROM statement specifies the location or table from which the data needs to be retrieved.
- WHERE Clause
The WHERE clause is used for filtering data. It allows you to specify conditions that the data must meet to be included in the query results. This is crucial for isolating relevant data.
- GROUP BY Clause
The GROUP BY clause is used for data aggregation. It allows you to group rows with similar values in one or more columns and perform aggregate functions (e.g., SUM, AVG, COUNT) on those groups.
- JOIN Operations
SQL supports different types of joins, including INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN, which enable you to combine data from multiple tables based on specified criteria.
- ORDER BY Clause
The ORDER BY clause is used to sort query results in ascending or descending order based on one or more columns. It’s used for arranging data for analysis.
- Mathematical and Statistical Functions
SQL provides a variety of built-in functions for performing mathematical and statistical calculations on data, such as SUM, AVG, MAX, MIN, STDDEV, and VARIANCE.
- Date and Time Functions
SQL offers functions for handling date and time data, allowing for time-series analysis, date arithmetic, and formatting.
- Subqueries
Subqueries, or nested queries, enable you to use the result of one query as input for another query. This is useful for complex data analysis tasks.
- Data Modification Statements
SQL not only retrieves data but also allows you to modify data using statements like INSERT, UPDATE, and DELETE. This is important for data preparation and cleaning.
- Window Functions
Window functions, like RANK(), LEAD(), and LAG(), are useful for performing calculations across rows within a specific window or partition of data.
- Stored Procedures and User-Defined Functions (UDFs)
SQL databases often support the creation of stored procedures and user-defined functions. These can be used to encapsulate complex analysis logic for reuse.
- Indexing
SQL databases provide indexing mechanisms to improve query performance, making data retrieval faster, which is crucial for large datasets.
- Reporting Tools
Many SQL-based database management systems offer reporting and visualisation tools that allow you to create charts, graphs, and reports based on SQL query results.
These tools in SQL and features make it a versatile and powerful language for data analysis, allowing analysts to retrieve, manipulate, and gain insights from data stored in relational databases.
Frequently Used SQL Queries in Data Analytics
What is an SQL Query?
An SQL query is a command used to interact with relational databases. It can be used to retrieve, filter, modify, or delete data in the database, as well as insert new data records. SQL queries are essential for managing and querying data in relational database management systems, allowing users to perform various tasks related to data manipulation and retrieval.
- The Function of ‘SELECT’ and ‘FROM’ Queries
The SELECT keyword is used to retrieve data from a given database. Data can be retrieved as a whole by viewing all the data in the table or specifying the column name of the data that is required.
The FROM keyword specifies which table the data is to be obtained from. Take this example below; this is a list of every customer in the database with their information.
| Customer ID | Customer Name | City | Country | Age | Sex | |
|---|---|---|---|---|---|---|
| 1211 | Ankit | xxxx@xxxx.com | Paris | Fr | 32 | Male |
| 1212 | Pallavi | yyyy@yyyy.com | London | UK | 42 | Female |
| 1213 | Matt | zzzz@zzzz.com | New York | US | 28 | Male |
| 1214 | Sahil | aaa@bb.com | Sydney | AU | 32 | Male |
| 1215 | Aarti | bbb@ccc.com | Washington DC | US | 24 | Female |
To get the list of names and email IDs of the customer from the table, the code to be used is:
And the output for the given code would be:
| Customer Name | |
| Ankit | xxxx@xxxx.com |
| Pallavi | yyyy@yyyy.com |
| Matt | zzzz@zzzz.com |
| Sahil | aaa@bb.com |
- The Function of ‘WHERE’, ‘AND’, and ‘OR’ Queries
In situations requiring specific data criteria, the WHERE clause is used. It filters records and only extracts data that fulfils the specified conditions.
In the table from the previous example, suppose only the US clients were to be selected; the code would be as follows:
SELECT Customer_Name, Email
FROM Customers
WHERE Country = ‘US’;
The output for the given code would be:
Matt: zzzz@zzzz.com
Aarti: bbb@ccc.com
The WHERE clause retrieves data that meets the criteria, and does not display the Country column in the results because it is used only to identify the specific entries.
Multiple criteria can also be met simultaneously, using the AND clause. Suppose the data needed was specific only to women from America, the code would be:
SELECT Customer_Name, Email
FROM Customers
WHERE Country = ‘US’
AND Sex = ‘Female’;
Giving the output:
Aarti: bbb@ccc.com
OR is another query that is used, wherein if either of the criteria of the WHERE query is true, then those results come back. An example of this would be:
- The Function of ‘ORDER BY’ and ‘ASC/DESC’ Queries
ORDER BY is a clause used to sort the result set of a query in a specific order. By default, it arranges the values in an ascending order, but can be instructed to do it in a descending order using the DESC clause.
Here is a table to demonstrate an example of the usage of this:
| Order Id | Item | Quantity | Customer Id | Total Value |
|---|---|---|---|---|
| 24 | A625 | 1 | 1213 | $100 |
| 23 | Ae35 | 3 | 1214 | $210 |
| 14 | A212 | 4 | 1214 | $249 |
| 12 | AXX1 | 4 | 1213 | $212 |
The code to place the values in the above table in a descending order would be written as:
SELECT *
FROM Orders
ORDER BY Total_Value DESC;
Here, the * is used to instruct the SQL engine to obtain all columns for the table, so the result will be:
| Order Id | Item | Quantity | Customer Id | Total Value |
|---|---|---|---|---|
| 14 | A212 | 4 | 1214 | $249 |
| 12 | AXX1 | 4 | 1213 | $212 |
| 23 | Ae35 | 3 | 1214 | $210 |
| 24 | A625 | 1 | 1213 | $100 |
- The Function of ‘CASE’, ‘WHEN’, ‘THEN’, and ‘ELSE’ Queries
The CASE query is used to evaluate a conditional statement until it finds it to be true, after which it displays the results accordingly. If a true statement isn’t found, an optional ELSE command can be added to the code to display a different result.
For example, orders in the above table can be segregated as high or low value based on whether they are more than or less than $150. Here’s the code for the same:
SELECT Order_Id, Total_Value
CASE WHEN Total_Value > 150 THEN ‘HIGH’
WHEN Total_Value < 150 THEN ‘LOW’
ELSE ‘MEDIUM’
END as Order_Category
FROM Orders;
| Order ID | Total Value | Order Category |
|---|---|---|
| 14 | $249 | High |
| 12 | $212 | High |
| 23 | $210 | High |
- The Function of ‘GROUP BY’ Query
The GROUP BY clause is used to group rows of data that have the same values in one or more columns into summary rows. It is typically used in conjunction with aggregate functions like SUM, COUNT, AVG, MAX, or MIN, accompanied by (), to perform calculations on the grouped data.
The below code is an example of how this code works, where the customer who had the highest order value is obtained:
SELECT Customer_Id, SUM(Total_Value)
FROM Orders
GROUP BY Customer_Id
ORDER BY 2 DESC;
The result will hence be:
Order ID: Total Value
1214: $459
1213: $312
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Conclusion
SQL can clarify complex queries, automate report writing and deliver real-time business intelligence all despite using a single common language. Why would you want to use SQL for data analysis? Because it allows you to quickly and powerfully handle thousands and millions of data points with the flexibility to perform a set of complex analyses repeatedly. Now that you have access to multiple SQL functions for data analysis, it will give you the power to clean, summarize, and transform your data in a way that you haven’t exercised before, making SQL one of the most useful tools for every analyst.
