Rookie SQL Mistakes That Can Cost You Time

As a junior data analyst, learning SQL can be both rewarding and challenging. It allows you to understand data and extract valuable insights, but common SQL mistakes often become frustrating barriers. In this guide, I’ve compiled the most frequent rookie SQL mistakes and how to avoid them, to help you save time and improve your skills more effectively.

Initially, learning SQL was often frustrating due to minor errors that took hours to debug. Simple oversights, such as a missing comma, or a misplaced alias, could completely derail an entire analysis and leave me feeling discouraged. However, these challenges ultimately proved to be valuable lessons, teaching me the importance of precision and how to write cleaner, more effective SQL code. You can read my story in this article.

I've faced the same struggles that many of you are going through. This guide is here to help you avoid common errors but, more importantly, it's about learning from these mistakes and building real confidence in your SQL skills.

1. Forgetting Commas in SELECT Lists

Forgetting to separate column names with commas in a SELECT statement is a frequent mistake that leads to syntax errors. This mistake occurs because SQL expects each column to be clearly defined and missing commas cause confusion for the parser.

Example:

SELECT first_name last_name FROM employees;

Error: Missing comma between first_name and last_name. SQL will interpret first_name last_name as first_name AS last_name, resulting in an unintended alias assignment. Assuming you need both first_name and last_name in the results, this will cause a problem, as only first_name with an alias will be retrieved.

Solution: Always double-check your SELECT statements to ensure all column names are separated by commas. Take your time when writing a query and consider reading it aloud or breaking it down to see if each element is properly separated. Additionally, review the query result to confirm that all the columns you intended to retrieve are present.

Corrected Example:

SELECT first_name, last_name FROM employees;

Read more about SELECT statements in this awesome article by Kateryna Koidan: How Do You Write a SELECT Statement in SQL.

2. Leaving Out Quotation Marks

When working with text values, leaving out quotation marks can lead to syntax errors. SQL requires text values to be enclosed in single quotes ( ‘ ) to differentiate them from column names or other keywords. In other programming languages, strings are often enclosed in double quotes ( “ ) but, in SQL, double quotes are used for identifiers (e.g., table or column names).

Example:

SELECT * FROM employees WHERE department = Sales;

Error: Missing quotation marks around Sales. Without quotes, SQL interprets Sales as a column name, which can lead to an error if such a column doesn’t exist.

Solution: Always use single quotes around text values in your queries. This ensures that SQL treats the value as a string, not as a column or keyword.

Corrected Example:

SELECT * FROM employees WHERE department = 'Sales';

Another Example:

SELECT * FROM employees WHERE department = "Sales";

Error: Using double quotes instead of single quotes makes SQL interpret "Sales" as an identifier, which can lead to an error if there is no column or table named Sales.

Solution: Always use single quotes around text values in your queries. This ensures that SQL treats the value as a string, not as a column or keyword.

3. Misspelling Table And Column Names

Misspelling table or column names is a common syntax mistake. SQL is not forgiving when it comes to typos—if the table or column name doesn’t match exactly, it will result in an error or incorrect results.

Example:

SELECT first_nam FROM employees;

Error: Misspelled first_name.

Solution: Always double-check table and column names before executing queries. Using an IDE or SQL editor that auto-completes names can help reduce these types of errors. You can also refer to the database schema frequently to verify the correct names.

Corrected Example:

SELECT first_name FROM employees;

4. Formatting Issues: Keep Your Code Clean

Poor query formatting makes code difficult to read and debug. This is particularly problematic when queries become complex, involving multiple tables or conditions. Clean code is essential for efficient collaboration and troubleshooting.

Example:

SELECT first_name,last_name,department FROM employees WHERE department='Sales' AND salary>50000;

Error: Hard to read and debug.

Solution: Format your queries with indentation and line breaks. This helps break down different clauses, making the code easier to understand at a glance. Proper formatting also makes it easier to spot errors.

Formatted Example:

SELECT first_name, last_name, department
FROM employees
WHERE department = 'Sales'
  AND salary > 50000;

Want to write good SQL code? Read 20 Basic SQL Query Examples for Beginners by Tihomir Babic.

5. Incorrect Ordering of Statements

SQL requires a specific order for its clauses, such as SELECT, FROM, WHERE, and ORDER BY. If the order is incorrect, SQL will generate a syntax error or behave unexpectedly. This is a common problem for beginners, who are not yet familiar with the structure of SQL statements.

Example:

FROM employees SELECT first_name, last_name;

Error: Incorrect order of FROM and SELECT.

Solution: Familiarize yourself with the correct order of SQL clauses: SELECT, FROM, WHERE, GROUP BY, HAVING, ORDER BY. Practice writing queries in this sequence until it becomes second nature.

Corrected Example:

SELECT first_name, last_name FROM employees;

More about that in the article SQL Order of Operations by Ignacio L. Bisso.

6. Improper Alias Usage

Aliases are useful for shortening long table names, but using them incorrectly can lead to confusing, unreadable queries. Aliases should make queries easier to understand, not harder.

Example:

SELECT a.first_name, a.last_name FROM employees a;

Issue: The alias a is not descriptive, making the query harder to understand.

Solution: Use meaningful aliases that clearly convey what the table represents. For example, use emp for employees to maintain clarity.

Corrected Example:

SELECT emp.first_name, emp.last_name FROM employees emp;

More resources about aliases in SQL:

• How to Use Aliases in SQL Queries
• How to Use Aliases with SQL JOINs

7. Syntax Errors

Syntax errors, such as forgetting to close parentheses or incorrect use of commas, are common among beginners. These errors can make a query fail to execute or lead to unexpected results.

Example:

SELECT COUNT(* FROM employees;

Error: Missing closing parenthesis after the *. SQL functions like COUNT() require opening and closing parentheses.

Solution: Use an IDE that highlights syntax issues as you type. This will help you catch missing parentheses or misplaced commas early. Always review your queries before execution.

Corrected Example:

SELECT COUNT(*) FROM employees;

Another example

SELECT * FROM employees WHERE department = 'Sales;

Error: Missing closing single quote after Sales. This causes SQL to interpret the rest of the query as part of the string.

Solution: Always use matching pairs of single quotes for string literals.

Corrected Example:

SELECT * FROM employees WHERE department = 'Sales';

Third example

SELECT name, order FROM orders;

Error: order is a reserved keyword in SQL and cannot be used as a column name without proper handling.

Solution: If you must use a reserved keyword as a column name, enclose it in double quotes ("order") or square brackets ([order]), depending on your SQL dialect. However, it's better to avoid using reserved words as identifiers altogether.

Corrected query:

SELECT name, "order" FROM orders;

8. Join Logic: Understanding The Basics

Incorrect joins often result from a misunderstanding of how different join types work or placing filtering conditions incorrectly. This can lead to incorrect results, missing data, or bloated datasets.

Example:

SELECT * FROM orders JOIN customers;

Error: Missing ON condition for the join. Without a join condition, SQL doesn’t know how to match rows from the two tables.

Solution: Always specify a join condition using the ON clause. Understand the difference between INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN, and use them as needed.

Corrected Example:

SELECT * FROM orders
JOIN customers ON orders.customer_id = customers.customer_id;

Want to be fluent in SQL JOINs? Check out my Complete Guide to SQL JOINs. This article covers everything you need to know about using data in your query from more than one table.

9. Mishandling NULL Values

NULL values can be tricky to handle, and assuming that NULL is the same as zero or an empty string is a common mistake. This can lead to incorrect aggregations or misleading conclusions.

Example:

SELECT * FROM employees WHERE department != NULL;

Error: NULL cannot be compared using standard comparison operators.

Solution: Use functions like IS NULL or IS NOT NULL to properly handle NULL values. Additionally, use COALESCE() to replace NULL with a default value when needed.

Corrected Example:

SELECT * FROM employees WHERE department IS NOT NULL;

More about NULLs:

• What Is a NULL in SQL?
• How to Find the Next Non-NULL Value in SQL
• How to Use Comparison Operators with NULLs in SQL
• What Is a NOT NULL Constraint in SQL?

10. Assuming Data Integrity

Assuming that data is clean without validation is risky. Data may contain duplicates, NULL values, or incorrect formats, leading to incorrect query results.

Example:

SELECT * FROM orders WHERE order_date > '2024-01-01';

Issue: Assuming all order dates are in the correct format and none are NULL. If order_date is of type DATE, the database will ensure that the values are valid dates, but there could still be NULL values or unexpected edge cases.

Solution: Validate data before analysis. Check for NULL values, duplicates, and ensure the format is consistent. Use checks or constraints to ensure data quality.

Validation Example:

SELECT * FROM orders WHERE order_date IS NOT NULL AND order_date > '2024-01-01';

Avoiding These Mistakes For Better Efficiency

Making mistakes is a natural part of learning, but understanding and avoiding common SQL pitfalls will help you become a more effective data analyst. Writing readable queries, using consistent naming, understanding joins and NULL values, validating data, and being mindful of syntax can save you from hours of frustration and rework.

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