SQL For Beginners: Essential Queries, Joins, Indexing & Optimization Tips

Did you know? The demand for SQL skills has increased by 30% over the past year, with 78% of companies highlighting SQL expertise as a top priority for data-centric positions.

Many businesses like Amazon today rely on SQL to manage and query vast amounts of customer data, product inventories, and transaction histories. SQL enables efficient retrieval of data, helping them personalize user experiences, track purchases, and optimize product recommendations. 

This SQL cheat sheet is designed to help you understand key SQL queries and optimize database performance with indexing, joins, and advanced functions.

SQL Basics: A Cheat Sheet for Beginners

SQL is the language that helps you communicate with databases, retrieve information, and modify data as needed. If you're diving into the world of data, understanding SQL basics and SQL syntax is crucial. 

Relational Databases & SQL: A Quick Introduction

Relational databases store data in tables, much like a spreadsheet. These tables are made up of rows (records) and columns (attributes or fields), where each column stores a specific type of information.

• Tables: Imagine a table like a well-organized file cabinet. Each drawer (table) holds specific data, and the rows inside each drawer are the records.
• Rows: A row is a single record. For example, in a customer database, each row represents an individual customer’s information—like name, email, and address.
• Columns: Each column represents a type of data. In our customer example, columns might include "Name," "Email," "Address," and "Phone Number."

SQL allows you to interact with these tables—whether it’s retrieving, inserting, or updating data. The beauty of SQL is its ability to work with relational databases, where tables are linked together through common fields. 

This structure is particularly useful for complex data relationships, like tracking customer orders or managing inventory systems.

In real-life database applications, it’s common to have multiple tables that need to be connected. This is where foreign keys come in. A foreign key is a column or set of columns in one table that links to the primary key of another table. This establishes relationships between data across tables.

For example, imagine a Customers table and an Orders table. The Customers table has a primary key. This is a column, usually the customer_id, that uniquely identifies each customer. The Orders table contains a foreign key—the customer_id—which links each order to a specific customer. This relationship ensures that each order can be traced back to the customer who placed it.

Primary Key Constraints are essential because they ensure that each record in a table is unique. This is important because it allows for precise and reliable data retrieval. Without a primary key, you might have duplicate records, making it difficult to track data accurately.

You can better understand the relationship between relational databases and SQL with upGrad’s online software programming courses. The hands-on training will significantly strengthen your grip on SQL basics. Besides, the recognized certifications can give you a major edge in your career. 

Also Read: Top SQL Queries in Python Every Python Developer Should Know 

Popular SQL Database Management Systems

There are several widely-used database management systems (DBMS) that implement SQL. Each is designed to suit different needs, from small businesses to large corporations.

• MySQL: Often used for web development and small to medium-sized businesses. It’s fast, open-source, and works well with popular web frameworks like WordPress or Django.
• PostgreSQL: A powerful, open-source DBMS that’s great for complex queries, large datasets, and high-performance needs. It’s ideal for applications that require advanced functionality like JSON storage or geospatial data.
• SQL Server: A Microsoft product widely used in enterprise environments, particularly where other Microsoft technologies (like .NET) are in use. It integrates well with other Microsoft software, making it a popular choice for large businesses.
• Oracle: Known for handling large volumes of data and high-transaction environments. Oracle is often used by large corporations in finance, healthcare, and retail, where database uptime and reliability are critical.

Example: An e-commerce platform might choose MySQL because of its open-source nature and ease of use, making it a cost-effective solution for their product catalog and customer data. 

Meanwhile, a large banking institution might use Oracle for its ability to handle complex financial transactions and maintain data security at scale.

Also Read: Most Asked Oracle Interview Questions and Answers - For Freshers and Experienced

Basic SQL Syntax & Commands

Now, let’s break down some of the most important SQL commands that you’ll use regularly. These basic commands are the foundation for interacting with databases:

• SELECT: Retrieves specific data from a database table.
• INSERT: Adds new data into a table.
• UPDATE: Modifies existing data in a table.
• DELETE: Removes data from a table.

Example: Let’s say you're working for an online bookstore. You have a table called Books, and you want to retrieve the titles and prices of books that are on sale. Here’s how you’d write the SQL query:

SELECT title, price FROM Books WHERE discount > 0; 

This command retrieves the "title" and "price" columns from the Books table where the discount is greater than 0 (meaning the book is on sale).

Output: You’ll get a list of books with their prices that are currently discounted.

Another example could be updating a customer’s shipping address in an Orders table:

UPDATE Orders SET shipping_address = '123 New Street' WHERE order_id = 101;

This query updates the shipping address for the order with the ID 101. It’s useful in cases where customers change their delivery details after placing an order.

And finally, removing a record from the Orders table:

DELETE FROM Orders WHERE order_id = 101;

This command deletes the order with ID 101 from the database. This might be necessary if an order was canceled or entered incorrectly.

SQL syntax is straightforward, but the magic happens when these basic commands are combined in more complex queries to analyze data and extract meaningful insights.

Note: Missing WHERE clauses in SQL UPDATE or DELETE statements can lead to unintended changes or data loss. For example, running DELETE FROM table_name without a WHERE clause will remove all rows in the table, which can be disastrous in real-world scenarios. Always double-check your queries before executing them to avoid costly mistakes!

Did you know? SQL was first developed in the 1970s by IBM researchers as a way to manage and manipulate data in relational databases. Over time, it has become the standard language for working with databases in nearly every industry.

As SQL crosses paths with the latest AI and data science techniques to deliver better outcomes, building the most relevant skills can be useful. upGrad’s Executive Post Graduate Certificate Programme in Data Science & AI will give you hands-on training in advanced topics like deep learning, data engineering, and Generative AI. 

Also Read: Top 27 SQL Projects in 2025 With Source Code: For All Levels

Next in this SQL cheat sheet for beginners, let’s explore the most popular SQL queries for database operations.

SQL Queries for Database Operations

Did you know that an estimated 7 million people use SQL? That’s because SQL is the go-to tool for accessing and manipulating data, making it indispensable in data-driven decision-making. 

Being able to query your database with precision can help you unlock key insights quickly, optimize business processes, and improve overall productivity. In fact, companies that use data analytics to guide their decisions are five times more likely to make faster decisions than their competitors. 

Understanding these SQL queries is a competitive advantage that can benefit you in your career.

Data Retrieval Queries

To start, let’s look at how you retrieve data from a database using SQL. But as beginners advance, they’ll need to handle more complex filtering in SQL queries. This includes using nested conditions (like AND, OR, and NOT) and joins to combine data from multiple tables. 

Mastering these techniques will enable you to retrieve more precise and meaningful results, especially when working with relational databases that store data across various tables.

 The basic components are:

• SELECT: Retrieves specific data from a table.
• FROM: Specifies the table to retrieve data from.
• WHERE: Filters the results based on a condition.
• ORDER BY: Sorts the results.
• LIMIT: Limits the number of results returned.

Example: Imagine you’re working for an online store and need to retrieve customer details from the Customers table.

SELECT first_name, last_name, email FROM Customers WHERE country = 'USA' ORDER BY last_name LIMIT 10;

This SQL SELECT query selects the first name, last name, and email of customers from the USA. The results are ordered by last name, and only the first 10 records are returned.

Output: A list of the first 10 customers from the USA, sorted by last name.

Did you know? The SQL SELECT query is the most frequently used SQL command. It’s so essential that nearly every SQL-based query starts with it!

 Filtering Data Efficiently

There are several ways of SQL filtering data beyond the basic WHERE clause. These commands help you narrow down results more effectively:

• LIKE: Used for pattern matching (often with wildcards like %).
• IN: Checks if a value matches any value within a list.
• BETWEEN: Filters results within a specified range.
• DISTINCT: Removes duplicate values from your results.

Example: If you’re working in HR and need to find employees with salaries between $40,000 and $60,000:

SELECT first_name, last_name, salary FROM Employees WHERE salary BETWEEN 40000 AND 60000;

This query returns employees whose salaries fall between $40,000 and $60,000.

Output: A list of employees with their first name, last name, and salary in the specified range.

Note: The BETWEEN operator in SQL is inclusive, meaning it includes both the starting and ending values. So, a salary of $40,000 and $60,000 would be included in the results.

Aggregations & Grouping

When you need to summarize data, SQL aggregate functions come in handy. These functions allow you to calculate totals, averages, and counts, among other things:

• COUNT(): Counts the number of rows in a set.
• SUM(): Adds up values.
• AVG(): Calculates the average of a set of values.
• MIN(): Finds the smallest value.
• MAX(): Finds the largest value.
• GROUP BY: Groups rows sharing a property (like region or product).
• HAVING: Filters groups after aggregation.

Example: Let’s say you want to calculate the total sales by region in your company’s sales data:

SELECT region, SUM(sales) AS total_sales FROM Sales GROUP BY region HAVING total_sales > 10000;

This query groups sales by region, sums the sales for each region, and filters out regions where total sales are less than $10,000.

Output: A list of regions where total sales exceed $10,000, along with the total sales for each region.

Note: GROUP BY is often used in conjunction with aggregation functions like SUM() or AVG() to analyze data across categories, such as by region, department, or time period.

Data Modification Commands

SQL isn’t just for querying data. You can also modify the data within your tables using commands like INSERT INTO, UPDATE, and DELETE.

• INSERT INTO: Adds new rows to a table.
• UPDATE: Modifies existing data.
• DELETE: Removes data from a table.
• TRUNCATE: Deletes all rows in a table (but doesn’t remove the table itself).

Example: If you need to add a new customer to the Customers table:

INSERT INTO Customers (first_name, last_name, email, country) 
VALUES ('John', 'Doe', 'john.doe@example.com', 'USA');

This query inserts a new record into the Customers table with the specified values for first name, last name, email, and country.

Output: A new row is added to the table with John Doe's information.

Or, if you need to update an employee’s salary:

UPDATE Employees SET salary = 55000 WHERE employee_id = 123;

This query updates the salary of the employee with employee_id = 123 to $55,000.

Output: The salary for that employee is updated in the database.

Did you know? The TRUNCATE command is much faster than DELETE because it doesn’t log individual row deletions. However, it’s more destructive—there’s no way to undo it once executed!

As you progress, you’ll learn more advanced techniques to make your queries even more efficient. For now, keep practicing these foundational queries, and you’ll quickly become proficient in managing and analyzing data with SQL.

You can also deepen your knowledge with upGrad’s free Advanced SQL: Functions and Formulas course. Learn window functions, aggregations, and complex calculations to optimize queries, boost performance, and extract deeper insights effortlessly.

Also Read: SQL for Data Science: A Complete Guide for Beginners

Now that we’ve covered the basic SQL queries for database operations, let’s explore SQL joins, which will help you merge data from different tables.

Understanding SQL Joins: Merging Data from Multiple Tables

Did you know that SQL joins are used in almost all queries in relational databases? That’s because they allow you to answer complex questions by merging data from different sources in one go.

SQL joins can help you combine data from multiple tables into a single result set, leading to SQL query optimization. Think of it like connecting the dots between two or more pieces of information stored in separate places. 

Types of Joins & When to Use Them

When you need to pull related data from different tables, such as combining customer information with their order history, SQL joins are the way to go. In this SQL joins tutorial, let’s break down the different types with SQL self join examples.

1. INNER JOIN: This is the most common type of join. It retrieves only the records where there’s a match in both tables. If you’re looking for data that exists in both tables, this is your go-to join.

Example: You have two tables: Customers and Orders. You want to find customers who have made a purchase.

SELECT Customers.name, Orders.order_id 
FROM Customers 
INNER JOIN Orders ON Customers.customer_id = Orders.customer_id;

This query retrieves the customer name and their order ID, but only for customers who have placed an order. If a customer hasn’t made an order, they won’t appear in the result set.

Output: A list of customers who have made purchases, along with their order IDs.

Did you know? The INNER JOIN is the default join in many SQL tools. It’s used when you want to filter out rows that don’t have corresponding matches in the other table.

2. LEFT JOIN: This join retrieves all records from the left table, and only the matching records from the right table. If there’s no match, you’ll get NULL values for the columns from the right table.

Example: You want to get a list of all customers and their orders, even if some customers haven’t placed any orders yet.

SELECT Customers.name, Orders.order_id 
FROM Customers 
LEFT JOIN Orders ON Customers.customer_id = Orders.customer_id;

This query returns all customers, along with their order IDs if they have placed an order. If a customer hasn’t ordered anything, the order ID will be NULL.

Output: A list of all customers, with matching order IDs where applicable.

3. RIGHT JOIN: This is similar to a LEFT JOIN, but it retrieves all records from the right table, and only the matching records from the left table.

Example: Now you want to see all orders, including those that didn’t have a matching customer (in case there are any orphaned records in your database).

SELECT Customers.name, Orders.order_id 
FROM Customers 
RIGHT JOIN Orders ON Customers.customer_id = Orders.customer_id;

This query returns all orders, and the corresponding customer names if they exist. If an order doesn’t have a matching customer, the customer name will be NULL.

Output: A list of all orders, with customer names where available.

4. FULL OUTER JOIN: This join combines the results of both the LEFT JOIN and the RIGHT JOIN. It retrieves all records from both tables, with matching records where available. If there’s no match, it fills in NULL values.

Example: You want to get a full list of both customers and orders, including customers who haven’t made any orders and orders without matching customers.

SELECT Customers.name, Orders.order_id 
FROM Customers 
FULL OUTER JOIN Orders ON Customers.customer_id = Orders.customer_id;

This query returns all customers and all orders. Where there’s no match, NULL values are used for missing data.

Output: A comprehensive list of all customers and orders, with NULL values where no match exists.

5. CROSS JOIN: This type of join generates a Cartesian product, meaning it returns all possible combinations of records between the two tables.

Example: You have a table of products and a table of discounts. You want to create a list of all possible product-discount combinations.

SELECT Products.product_name, Discounts.discount_percentage 
FROM Products 
CROSS JOIN Discounts;

This query returns every combination of product and discount. If you have 10 products and 5 discounts, the result will include 50 combinations.

Output: A list of all product-discount pairs.

Did you know? CROSS JOIN can quickly generate large datasets, so use it with caution. It’s not typically used for regular data analysis but can be helpful for generating test data or combinations.

In the case of INNER JOIN vs. LEFT JOIN, an INNER JOIN returns only the rows where there is a match between the two tables, excluding unmatched records. 

On the other hand, a LEFT JOIN (or LEFT OUTER JOIN) returns all the rows from the left table and the matched rows from the right table. If there’s no match, the result will include NULL values for the right table’s columns.

SQL UNION vs. JOIN: When to Use Which?

A common question is when to use UNION versus JOIN. Both combine data, but in different ways.

Here’s a table outlining the main differences between them:

This table should help you clearly determine when to use JOIN versus UNION depending on the task at hand.

Example: Let’s say you want to merge sales data from two different years:

SELECT product_name, sales FROM Sales_2022
UNION
SELECT product_name, sales FROM Sales_2023;

This query combines the sales data from 2022 and 2023 into one result set. Each product’s sales will be stacked under each other.

Output: A list of products and sales from both years.

Note: The UNION operator removes duplicates by default. If you want to keep duplicates, use UNION ALL instead.

With a solid understanding of how and when to use each join type, you'll be able to achieve SQL query optimization and realize maximum efficiency.

You can also get an in-depth overview into database design with upGrad’s free Introduction to Database Design with MySQL course. This introductory course can kickstart your data analytics journey, covering database design and MySQL basics using MySQL Workbench

Also Read: Is SQL Hard to Learn? Challenges, Tips, and Career Insights

Next, let’s look at how you can optimize database queries with indexes.

How to Create an Index in SQL?

When you're working with large datasets, performance is key. SQL indexing is one of the most effective ways to speed up query execution, especially when you need to retrieve data quickly. 

Think of an index like an index in a book—it helps you find the right page (or data) without having to scan the entire book (or table). By using indexes, you can improve the speed of your queries and make your database much more efficient.

Fact: A well-designed index can speed up data retrieval significantly, but a poorly designed one can actually slow down your queries!

In this SQL indexing tutorial, let’s dive into how to create an index in SQL, the types of indexes available, and when to use them for optimal performance.

Types of Indexes in SQL

Imagine you're searching for a book in a library. If you go through every book on the shelf, it will take forever. But if there’s an index at the front of the library that lists all books by title, you can go straight to the book you’re looking for. That's what indexes do for your database—help you find data faster.

Let’s start with understanding the difference between clustered vs. non-clustered indexes:

• A clustered index determines the physical order of data in a table. There can only be one clustered index per table.
• A non-clustered index creates a separate data structure from the table and stores the indexed values with pointers to the data. You can have multiple non-clustered indexes on a table.

Now, let’s explore them on a deeper level and understand their role in SQL performance tuning.

1. Non-Clustered

If you have a Customers table and frequently search for customers by last_name, you might create a non-clustered index on the last_name column for faster searches. The actual customer data, however, is stored in the table in its original order, which is determined by the clustered index (usually on the primary key like customer_id).

CREATE NONCLUSTERED INDEX idx_last_name
ON Customers (last_name);

This creates a non-clustered index on the last_name column in the Customers table. Non-clustered indexes improve query performance when searching for a specific last_name in the Customers table, without altering the actual order of data in the table. 

If you frequently query customer details based on last_name, this index speeds up those queries.

Expected Output: After executing the above SQL, SQL Server will create an index on the last_name column. This index will store the values of the last_name column along with a reference to the corresponding rows in the Customers table. 

The data itself is not reordered; instead, the index allows the database engine to quickly locate rows based on last_name.

When you run a query like:

SELECT * FROM Customers WHERE last_name = 'Smith';

The index will allow the database to quickly locate all customers with the last name 'Smith' rather than scanning every row in the Customers table.

2. Unique Index 

If you want to ensure no two customers can have the same email address, you can create a unique index on the email column:

CREATE UNIQUE INDEX idx_unique_email
ON Customers (email);

This creates a unique index on the email column in the Customers table. It ensures that each value in the email column is unique across all rows in the table. If an attempt is made to insert a duplicate email, the database will raise an error.

This is typically used on fields like email, username, or any other field where uniqueness is required.

Expected Output: After executing the SQL, the email column will now be constrained to unique values, ensuring that no two customers can have the same email. If you try to insert a row with a duplicate email, you will get an error like:

Error: Violates unique constraint on column 'email'

3. Composite Index 

If you often search for orders based on both customer_id and order_date, you can create a composite index:

CREATE INDEX idx_customer_order
ON Orders (customer_id, order_date);

This creates a composite (multi-column) index on the customer_id and order_date columns in the Orders table.

Composite indexes are useful for queries that filter or sort by multiple columns. In this case, if you often run queries that filter by customer_id and order_date, this index will speed them up by allowing the database to look at both columns together.

Expected Output: After executing the command, the database will create an index that covers both the customer_id and order_date columns. This is beneficial for queries such as:

SELECT * FROM Orders
WHERE customer_id = 101 AND order_date >= '2023-01-01';

Without the composite index, the database would have to search through all orders to find those for customer 101 and with a date after January 1st. With the index, the database can quickly locate the rows where both conditions are met, improving query speed.

4. Full-Text Index 

In an e-commerce database, if you want to allow users to search for products based on descriptions, you could use a full-text index:

CREATE FULLTEXT INDEX idx_product_description
ON Products (description);

This creates a full-text index on the description column in the Products table. Full-text indexing is used for searching large text fields.

It allows for more advanced searches, such as finding products based on partial text matches or specific keywords within the description column.

If you're running searches for products based on their descriptions, a full-text index speeds up these operations by using specialized algorithms designed for textual data.

Expected Output: After executing the SQL, the Products table will have a full-text index that allows you to search the description column more efficiently.

Example of a full-text search query:

SELECT product_name 
FROM Products 
WHERE CONTAINS(description, 'wireless AND headphones');

This query would return products whose descriptions contain both "wireless" and "headphones" (note that full-text searches can use advanced text search features like AND, OR, and NEAR).

Here are some best practices you can follow to use these indexes effectively:

• Focus on columns that are used often in WHERE, ORDER BY, or JOIN clauses.
• While indexes speed up queries, they can also slow down INSERT, UPDATE, and DELETE operations. Too many indexes can harm performance.
• Create composite indexes only when necessary, as they take up more storage and can slow down updates.
• Regularly monitor query performance and remove unused or redundant indexes.

Did you know? Indexes are stored in memory, so if you have too many indexes, they can use up valuable memory and slow down your system.

SQL Query Optimization Techniques

When you’re working with databases, query performance is crucial. A slow-running query can impact your application, delay response times, and degrade the user experience. SQL query optimization isn’t just about making them work; it’s about making them work efficiently. 

Now, let’s walk through common techniques for performance tuning SQL queries and ensure your database functions at its best.

Did you know? A poorly optimized query can be 100 times slower than an optimized one, especially when dealing with large datasets. Optimizing your queries can save you a lot of time and resources!

Let’s dive into the techniques that will help speed up your SQL queries.

1. Optimizing SELECT Statements for Faster Data Retrieval

The SELECT statement is one of the most commonly used queries. However, selecting more data than you need can slow things down. It’s important to always be specific about what data you’re retrieving.

Example: Let’s say you only need the name and email of your customers from the Customers table. Instead of selecting everything with SELECT *, only retrieve the columns you actually need.

SELECT name, email 
FROM Customers
WHERE country = 'USA';

This query retrieves the name and email of all customers from the Customers table where the country is 'USA'. This is more efficient than selecting all columns using SELECT.

Expected Output:

This result only includes the name and email columns, filtered by country = 'USA'. The query will run faster than selecting all columns (SELECT *), especially if the Customers table has many columns.

Note: Using SELECT * can cause your query to return too much data, slowing down performance, especially on tables with many columns or rows.

2. Using EXPLAIN PLAN to Analyze SQL Query Performance

Before optimizing a query, it’s important to understand its execution plan. EXPLAIN PLAN is a tool that shows you how SQL executes a query, allowing you to spot inefficiencies.

Example: Let’s say you want to optimize a query that takes too long. You can run:

EXPLAIN PLAN FOR 
SELECT name, email 
FROM Customers 
WHERE country = 'USA';

This command will return the execution plan, showing how SQL retrieves the data. You’ll see whether the database uses indexes or scans the entire table. If you see a full table scan, that’s a sign you need an index for the country column to speed things up.

Expected Output:

Did you know? The EXPLAIN PLAN helps you identify slow parts of a query, allowing you to fine-tune them. It’s like a roadmap for understanding your query’s journey!

3. Reducing Execution Time with Proper Indexing & Constraints

Indexes are crucial for speeding up query execution, but only when used wisely. Indexes should be created on columns that are frequently queried. Likewise, constraints like PRIMARY KEY or UNIQUE help enforce data integrity while optimizing access.

Example: You frequently run queries that filter customers by their email. Adding an index on the email column will improve performance.

CREATE INDEX idx_email 
ON Customers (email);

The index on email speeds up queries that filter or search by email, as the database will jump straight to the matching rows instead of scanning the entire table.

Expected Output:

The query is now faster because it uses the index on the email column to locate the row where email = 'john@example.com' without scanning the entire table.

Note: Indexes can improve query performance by 100 times in large tables, but too many indexes can slow down INSERT, UPDATE, and DELETE operations. It's a balance!

4. Avoiding SELECT * (Why Column Selection is Important)

When querying large tables, it’s tempting to use SELECT *, which retrieves all columns. However, retrieving unnecessary data slows down the query and wastes resources.

Example: If you only need the order_id and order_date for orders placed by a specific customer, don’t use SELECT *.

SELECT order_id, order_date 
FROM Orders 
WHERE customer_id = 101;

By selecting only the necessary columns (order_id and order_date), you reduce the amount of data retrieved, which speeds up the query.

Expected Output:

By selecting only the necessary columns, you avoid retrieving unnecessary data, which speeds up the query, especially in large tables.

Note: Selecting only the columns you need is one of the simplest ways to optimize a query. It reduces both IO and CPU usage.

5. Using Subqueries & CTEs (Common Table Expressions) for Readability

Sometimes, breaking complex queries into smaller pieces makes them more readable and easier to optimize. Subqueries and CTEs (Common Table Expressions) are great tools for this.

Subquery Example: Suppose you want to find customers who have placed more than 5 orders. You can use a subquery to first count the orders:

SELECT name, email
FROM Customers
WHERE customer_id IN (SELECT customer_id 
                      FROM Orders 
                      GROUP BY customer_id 
                      HAVING COUNT(order_id) > 5);

The subquery counts the number of orders per customer and returns those with more than 5. The main query then retrieves the corresponding customer information.

Expected Output:

The result shows customers who have placed more than 5 orders. The subquery first calculates the count of orders per customer, and the main query fetches the names and emails of customers who meet the condition.

CTE Example: A CTE makes it easier to organize complex queries, especially when dealing with multiple steps.

WITH OrderCounts AS (
    SELECT customer_id, COUNT(order_id) AS order_count
    FROM Orders
    GROUP BY customer_id
)
SELECT name, email
FROM Customers
JOIN OrderCounts ON Customers.customer_id = OrderCounts.customer_id
WHERE order_count > 5;

This CTE does the same thing as the subquery but is often more readable and reusable. You define the OrderCounts CTE once and use it in the main query.

Expected Output:

The CTE performs the same function as the subquery, but it’s more readable and reusable. The result is the same as the subquery example, showing customers who have placed more than 5 orders.

Note: CTEs are temporary result sets, making them more efficient for complex queries and improving query clarity. They can also be recursive, which is great for hierarchical data!

Here are some common SQL performance issues and their solutions:

Keep in mind that indexing, query structure, and filtering play key roles in ensuring that your database runs smoothly, even when handling large datasets.

Also Read: Attributes in DBMS: Types & Their Role in Databases

Next, let’s look at SQL transactions and how they can help you make changes in the database with ease.

Transactions & ACID Properties in SQL

When working with databases, it's crucial to make sure your data stays safe, consistent, and reliable, especially when you’re making updates or changes. SQL transactions group multiple operations into one unit, ensuring they either all succeed or none do. This helps prevent errors and data corruption.

SQL transactions allow you to group multiple operations together, so they either all succeed or none of them do. It's a way to ensure that data modifications are handled correctly, without errors or data corruption.

Did you know? Failures can occur in real-life applications. For example, if a network issue happens after a COMMIT, changes might be lost, leading to data inconsistencies. Using ROLLBACK ensures that partial changes can be undone if an error occurs before a COMMIT.

For example, imagine you're transferring money between two bank accounts. You’d want both operations (subtracting from one account and adding to the other) to either both succeed or both fail. That’s a transaction in action.

Let’s dive into the key concepts that make SQL transactions reliable and ensure database integrity: the ACID properties.

Understanding the ACID Model

The ACID model is the foundation of reliable database transactions. It ensures that database operations are processed in a way that maintains data integrity and consistency, even in the face of errors, crashes, or system failures. Each letter in ACID stands for one of the key properties that guarantee these outcomes:

Let’s break down each component:

Note: Atomicity ensures that if one part of a transaction fails, none of the changes are saved. It’s like hitting the "cancel" button during an online checkout before the purchase is finalized.

Using COMMIT & ROLLBACK to Manage Transactions

SQL provides two important commands for managing transactions: COMMIT and ROLLBACK.

• COMMIT: This saves all changes made during the transaction to the database.
• ROLLBACK: This undoes all changes made in the transaction, effectively canceling the operation.

Example: Imagine you’re updating a customer’s contact details. You start the transaction, make the changes, and then commit it:

BEGIN TRANSACTION;

UPDATE Customers 
SET email = 'newemail@example.com' 
WHERE customer_id = 101;

COMMIT;

Here, you begin a transaction, update the customer’s email, and commit the change. Once committed, the change is permanent.

But what if something went wrong—like the email format wasn’t valid? You can roll back the transaction:

BEGIN TRANSACTION;

UPDATE Customers 
SET email = 'invalidemail.com' 
WHERE customer_id = 101;

ROLLBACK;

This will undo the update, leaving the data unchanged.

Did you know? You can group multiple SQL operations into one transaction, and as long as all of them are successful, the COMMIT will ensure all changes are saved together. If one fails, the ROLLBACK will undo everything, keeping your database safe and consistent.

Deadlocks & How to Prevent Them in SQL

A deadlock happens when two or more transactions are waiting for each other to release resources, causing a standstill where no transaction can complete. 

For example, one transaction might be waiting to update a row that another transaction is holding, while the second transaction is waiting for a row held by the first one. This causes both transactions to be stuck indefinitely.

Here are a few tips on preventing deadlocks:

• Order your operations: Ensure that transactions access the same resources in the same order.
• Use shorter transactions: The less time a transaction holds onto resources, the less chance there is for a deadlock.
• Timeouts: Set time limits for transactions so they can be automatically rolled back if they take too long.

Note: Deadlocks are a common issue in high-traffic databases. However, with careful transaction management and proper resource locking, you can minimize the chances of them happening.

Example: Ensuring Safe Data Modifications with Transactions

Here’s an example where transactions ensure that data modifications are safe. Imagine you're processing an order where inventory and payment need to be updated.

If something goes wrong during payment, you wouldn’t want the inventory to be adjusted without the payment being confirmed.

BEGIN TRANSACTION;

UPDATE Inventory 
SET stock = stock - 1 
WHERE product_id = 501;

UPDATE Orders 
SET status = 'Processed' 
WHERE order_id = 123;

COMMIT;

The transaction updates both the inventory and the order status. If something goes wrong (like an issue with the payment), you can roll back the entire transaction to maintain consistency.

Example Output:

If the Inventory table had a row like this before:

After the UPDATE Inventory query, the stock for product_id = 501 would be reduced by 1:

If the Orders table had a row like this:

After the UPDATE Orders query, the status for order_id = 123 would change to 'Processed':

Once the COMMIT is executed, these changes become permanent.

With the ACID properties in place, you can be confident that your data is safe, consistent, and recoverable, even in the event of errors or system crashes. 

Also Read: Relational Database vs Non-Relational Databases-Key Differences Explained

Now that you have a good understanding of ACID properties in SQL, let’s explore 

Advanced SQL Functions & Stored Procedures

As you dig deeper into SQL, you’ll encounter powerful tools that go beyond basic queries. Advanced SQL functions and stored procedures can help you manipulate data in complex ways, automate processes, and improve performance. 

Did you know? SQL can do more than just retrieve data. With functions and stored procedures, you can automate tasks, manipulate strings, handle dates, and even run calculations across rows of data—all within your SQL queries.

Let’s walk through some of these powerful tools and how to use them effectively.

1. String Functions: Manipulating Text

SQL offers a variety of string functions that help you clean, format, and manipulate text data. Here are some of the most commonly used string functions:

• CONCAT(): Combines two or more strings into one.
• LENGTH(): Returns the length of a string.
• SUBSTRING(): Extracts a portion of a string.
• REPLACE(): Replaces occurrences of a substring with another substring.

Example: This code uses all four string functions (CONCAT(), LENGTH(), SUBSTRING(), and REPLACE()) together.

SELECT 
    CONCAT(first_name, ' ', last_name) AS full_name,
    LENGTH(first_name) AS first_name_length,
    SUBSTRING(last_name, 1, 3) AS last_name_prefix,
    REPLACE(email, '@example.com', '@newdomain.com') AS updated_email
FROM Customers
WHERE customer_id = 1;

• CONCAT() combines the first_name and last_name columns with a space in between to create a full_name. 
• LENGTH() calculates the length of the first_name column. This will return the number of characters in the first_name string.
• SUBSTRING() extracts the first 3 characters from the last_name column. This will return a substring starting at the 1st position and extracting 3 characters.
• REPLACE() replaces occurrences of @example.com in the email column with @newdomain.com. This demonstrates how you can update part of a string.

Expected Output: Let’s assume the Customers table has the following data for customer_id = 1:

After running the query, the result would look like this:

By using these functions together, you can manipulate and format your string data in a variety of useful ways!

Did you know? With CONCAT(), you can combine more than two strings! You can join any number of string values in one query.

2. Date & Time Functions: Working with Dates

Handling dates and times can be tricky, but SQL has several built-in functions that make it easy to work with temporal data. Here are some of the most useful date and time functions:

• NOW(): Returns the current date and time.
• CURDATE(): Returns the current date.
• DATE_ADD(): Adds a specified time interval to a date.
• DATE_DIFF(): Returns the difference between two dates.

Example: Here’s a combined SQL code that uses the date and time functions NOW(), CURDATE(), DATE_ADD(), and DATE_DIFF(), along with explanations and expected output.

SELECT 
    NOW() AS current_datetime,
    CURDATE() AS current_date,
    DATE_ADD(CURDATE(), INTERVAL 10 DAY) AS date_plus_10_days,
    DATE_DIFF(DATE_ADD(CURDATE(), INTERVAL 10 DAY), CURDATE()) AS days_difference;

• NOW() returns the current date and time in the format YYYY-MM-DD HH:MM:SS. It's useful when you need to get the exact date and time of the query execution.
• CURDATE() returns only the current date, with the format YYYY-MM-DD. It’s helpful when you only care about the date and not the time.
• DATE_ADD() adds a specific time interval to a date. In this case, it adds 10 days to the current date (CURDATE()). The result will be the date 10 days after today.
• DATE_DIFF() returns the difference between two dates. It calculates how many days there are between the date 10 days from now (generated by DATE_ADD()) and the current date (CURDATE()).

Expected Output: Let’s assume today’s date is 2025-04-03. After running the query, the output would look like this:

These functions are extremely useful when dealing with time-based calculations in SQL, such as scheduling, date-based filtering, and time intervals.

Did you know? You can add or subtract years, months, days, hours, or even minutes using DATE_ADD() or DATE_SUB(). SQL makes working with dates simple!

3. SQL Window Functions: Calculations Across Rows

SQL window functions are powerful tools that allow you to perform calculations across a set of rows related to the current row. These functions are often used for things like running totals, rankings, and moving averages. In this SQL window functions cheat sheet, let’s explore each of them in detail.

• RANK(): Assigns a rank to each row within a result set.
• DENSE_RANK(): Similar to RANK(), but doesn’t leave gaps in ranking when there are ties.
• ROW_NUMBER(): Assigns a unique number to each row in a result set.
• LAG(): Returns the value of a column from a previous row in the result set.
• LEAD(): Returns the value of a column from a following row in the result set.

Example: Here’s a combined SQL code example that uses the window functions RANK(), DENSE_RANK(), ROW_NUMBER(), LAG(), and LEAD() along with explanations and expected outputs.

SELECT 
    order_id,
    customer_id,
    amount,
    RANK() OVER (ORDER BY amount DESC) AS rank,
    DENSE_RANK() OVER (ORDER BY amount DESC) AS dense_rank,
    ROW_NUMBER() OVER (ORDER BY amount DESC) AS row_number,
    LAG(amount, 1) OVER (ORDER BY amount DESC) AS previous_amount,
    LEAD(amount, 1) OVER (ORDER BY amount DESC) AS next_amount
FROM Orders
ORDER BY amount DESC;

• The RANK() function assigns a rank to each row within a result set, ordered by the amount in descending order. If there are ties (i.e., rows with the same value), RANK() will leave gaps in the ranking.
• Similar to RANK(), but DENSE_RANK() does not leave gaps in the ranking when there are ties. For example, if two rows are tied at rank 1, the next row will have rank 2 (instead of rank 3 as in RANK()).
• This function assigns a unique number to each row, based on the order specified. Unlike RANK() or DENSE_RANK(), ROW_NUMBER() does not have ties and will assign a unique row number to every row in the result set.
• The LAG() function returns the value of a column from the previous row in the result set. It’s useful for comparing the current row’s value with the previous row’s value.
• The LEAD() function returns the value of a column from the next row in the result set. It’s useful for comparing the current row’s value with the next row’s value.

Example Data (for Orders table):

Expected Output:

These SQL window functions are incredibly powerful for analyzing ordered data, calculating running totals, and performing comparisons without needing to write complex subqueries.

Did you know? LEAD() and LAG() are useful for comparing values between rows. For example, LAG() could show you the sales from the previous day, and LEAD() could show you future sales.

4. Stored Procedures & Triggers: Automating Database Actions

Stored procedures are precompiled SQL code that you can execute with a single command. They allow you to automate common tasks and reuse logic. 

Triggers are similar, but they automatically execute in response to certain events, such as inserting, updating, or deleting data.

Example: Let’s say you want to automatically update the inventory whenever a new order is placed. You could use a trigger to reduce the stock whenever an order is inserted into the Orders table.

CREATE TRIGGER update_inventory
AFTER INSERT ON Orders
FOR EACH ROW
UPDATE Inventory
SET stock = stock - NEW.quantity
WHERE product_id = NEW.product_id;

This trigger runs after a new order is inserted into the Orders table. It updates the Inventory table, reducing the stock by the quantity ordered for the corresponding product.

Did you know? Triggers can be set to run before or after specific events, like INSERT, UPDATE, or DELETE. This helps automate workflows and keep your data consistent without manual intervention.

Here’s a list of all popular SQL functions:

With these advanced SQL functions and stored procedures, you can tackle complex data manipulation tasks, automate actions, and optimize your queries for better performance. 

Also Read: 52+ PL SQL Interview Questions Every Developer Should Know

Now that you’re familiar with the essential SQL functions, let’s move on to the common SQL errors and the corresponding debugging tips.

Common SQL Errors & Debugging Techniques

SQL errors can be frustrating, but they are an inevitable part of working with databases. The key to fixing them is understanding the common mistakes that happen and knowing how to approach debugging. 

Now, let’s  explore SQL error handling, fixing SQL queries, and debugging SQL syntax.

1. Syntax Errors: Missing Keywords, Incorrect Aliases

Syntax errors happen when SQL doesn’t understand your query due to incorrect structure. This can be something simple, like missing a keyword, or more complex issues like incorrectly using aliases.

Example: Let’s say you want to retrieve the names of employees and their departments from two tables: Employees and Departments. But you forget to include a JOIN keyword:

SELECT employee_name, department_name
FROM Employees, Departments
WHERE Employees.department_id = Departments.department_id;

Problem: This query will throw a syntax error because SQL doesn’t know how to combine the two tables without explicitly stating a JOIN.

Fix:

SELECT employee_name, department_name
FROM Employees
JOIN Departments ON Employees.department_id = Departments.department_id;

Explanation: By using the JOIN keyword, you clarify how to combine the tables based on the department_id.

Did you know? Missing keywords like JOIN, ON, or WHERE are often the culprit behind syntax errors. Double-check your SQL statement to make sure all the necessary components are included!

 2. Null Value Errors & How to Handle Them

Null values can lead to unexpected results or errors, especially if your query doesn’t account for them. Common issues occur when you try to perform operations on NULL values, such as attempting to add NULL to a number.

Example: You want to calculate the total sales for each employee, but some sales records have NULL values for the amount column:

SELECT employee_id, SUM(amount)
FROM Sales
GROUP BY employee_id;

Problem: If the amount column has NULL values, the sum might return NULL for some employees, which can lead to misleading results.
Fix:

Use COALESCE() to replace NULL with 0 when summing the values:
SELECT employee_id, SUM(COALESCE(amount, 0))
FROM Sales
GROUP BY employee_id;

COALESCE(amount, 0) replaces any NULL values in the amount column with 0, ensuring the sum is accurate.

Did you know? The COALESCE() function is a great tool for handling NULL values. It allows you to replace NULL with a default value, like 0 or an empty string, preventing errors in your calculations.

3. Primary Key & Foreign Key Violations

One of the most common errors in relational databases involves primary key and foreign key violations. These errors occur when you try to insert or update data in a way that violates referential integrity rules.

Example: You have two tables: Orders and Customers. The Orders table has a foreign key (customer_id) that references the Customers table's primary key (id). Now, you try to insert an order with a customer_id that doesn’t exist in the Customers table.

INSERT INTO Orders (order_id, customer_id, amount)
VALUES (101, 999, 500);

Problem: If 999 is not a valid customer_id in the Customers table, the query will fail with a foreign key violation error.

Fix: You need to either insert the valid customer_id or ensure that the referenced customer_id exists in the Customers table:

INSERT INTO Customers (id, name)
VALUES (999, 'John Doe');

INSERT INTO Orders (order_id, customer_id, amount)
VALUES (101, 999, 500);

By inserting a valid customer into the Customers table first, you can ensure that the customer_id in the Orders table is valid and the foreign key constraint is respected.

Note: Foreign key violations are common when data is inserted out of order or when relationships between tables aren’t properly maintained. Always ensure that referenced data exists before inserting or updating rows with foreign keys.

4. Fixing Slow Queries with Proper Indexing

A slow query is often caused by missing or inefficient indexing. When you query large tables without indexes, SQL has to perform a full table scan, which can be very slow.

Example: You frequently query the Orders table by customer_id. If there is no index on customer_id, the query will take longer as the database must scan the entire table.

SELECT customer_id, COUNT(order_id)
FROM Orders
GROUP BY customer_id;

Problem: This query might be slow on a large Orders table, especially without an index on customer_id.

Fix: Create an index on the customer_id column to speed up the query:

CREATE INDEX idx_customer_id ON Orders (customer_id);

The index on customer_id allows the database to quickly locate rows for each customer, significantly speeding up the query.

Did you know? Indexes can significantly improve the speed of data retrieval. However, creating too many indexes can slow down INSERT and UPDATE operations. It’s important to index only the most frequently queried columns.

Here’s what a SQL debugging workflow looks line:

1. Identify the Error: Carefully read the error message returned by SQL. It will often point you to the issue.

2. Fix Syntax Errors: Check for missing keywords, incorrect table aliases, or other syntactical mistakes.

3. Check Data Integrity: Ensure foreign key constraints are respected, and there are no violations of primary/foreign key rules.

4. Optimize with Indexes: Look for slow-running queries and add indexes to the most frequently queried columns.

Debugging SQL syntax is an essential skill, and with the right approach, you’ll be able to resolve issues quickly and get your queries working smoothly again.

Also Read: SQL Interview Questions & Answers from Beginner to Expert Guide

Now that you understand SQL error handling, fixing SQL queries, and debugging SQL syntax, let’s look at some of the SQL trends for this year.

SQL Trends & Future of Databases (2025 & Beyond)

Today, SQL databases are adapting to emerging technologies like cloud computing, artificial intelligence (AI), and machine learning (ML), integrating with new tools that provide more flexibility, real-time analytics, and even predictive capabilities.

For example, with the rise of cloud-based databases such as Amazon RDS, Azure SQL Database, and Google Cloud SQL, SQL databases can scale on demand without the need for physical infrastructure. 

Let’s look at the future of SQL and how it will evolve over this year.

The future of SQL databases is promising, with continuous innovations improving scalability, flexibility, and intelligence. As AI, cloud computing, and data relationships evolve, so will SQL, adapting to modern needs and staying relevant for years to come. 

Conclusion

SQL is crucial for modern businesses as it efficiently manages and analyzes structured data, powering everything from CRM systems to financial reporting. As organizations rely more on data for decision-making, SQL’s role in ensuring data integrity and accessibility remains essential.

With data-driven decision-making on the rise, expertise in SQL best practices opens doors to roles in data analysis, business intelligence, software development, and more. Now is the perfect time for mastering SQL queries and pursuing a career in SQL. You might be a beginner or an experienced developer, with the right learning resources, you are looking at a bright future with SQL skills.

Not sure where to start your SQL career? Connect with upGrad’s career counseling for personalized guidance.  You can also visit a nearby upGrad center for hands-on training to improve your Python skills and open up new career opportunities!

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