Top 10 Common SQL Mistakes Data Analysts Make (and How to Fix Them)

Introduction

SQL is the lingua franca of data analysis. Whether you’re pulling sales figures from a PostgreSQL warehouse or slicing click‑stream data in Snowflake, a single typo can turn a five‑minute query into a two‑hour debugging session.

A recent 2024 Stack Overflow Developer Survey showed that 51 % of respondents use SQL regularly, making it the second‑most popular language after JavaScript. Yet, even seasoned analysts stumble over the same pitfalls day after day.

In this article we’ll explore the top 10 SQL mistakes that data analysts make, illustrate each with real‑world examples, and provide clear, actionable fixes. By the end you’ll have a handy checklist to keep your queries accurate, performant, and easy to maintain.

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1. Forgetting Commas in SELECT Lists

Why it happens

When you type a long list of columns, it’s easy to miss a comma, especially when copying and pasting or using a spreadsheet export.

Example (error)

SELECT first_name last_name, email
FROM employees;

SQL interprets first_name last_name as first_name AS last_name, causing a syntax error or an unintended alias.

Fix

• Always separate column names with commas.
• Use an IDE or editor with SQL auto‑completion and syntax highlighting (e.g., VS Code with the SQLTools extension).
• Break long SELECT lists onto separate lines – this makes missing commas obvious.

SELECT
    first_name,
    last_name,
    email
FROM employees;

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2. Missing Quotation Marks Around String Literals

Why it happens

Analysts coming from Python or R often forget that SQL requires single quotes (') for string literals, while double quotes denote identifiers.

Example (error)

SELECT *
FROM orders
WHERE status = Completed;   -- no quotes

SQL looks for a column named Completed and throws invalid column name.

Fix

SELECT *
FROM orders
WHERE status = 'Completed';

Tip: Store frequently used strings in variables or CTEs to avoid repetition and typo‑related bugs.

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3. Misspelling Table or Column Names

Why it happens

Database schemas can be large, and a tiny typo (custmer_id instead of customer_id) leads to invalid identifier errors.

Example (error)

SELECT custmer_id, order_total
FROM orders;

Fix

• Use auto‑completion in your SQL client.
• Keep a schema reference sheet handy, or query the information schema:

SELECT column_name
FROM information_schema.columns
WHERE table_name = 'orders';

If you’re using a notebook, consider generating a list of columns programmatically and pasting it into your query.

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4. Poor Formatting and Lack of Indentation

Why it happens

When queries grow to multiple joins, sub‑queries, and window functions, a one‑liner becomes unreadable.

Example (hard to read)

SELECT a.id,b.name,c.amount FROM accounts a JOIN customers b ON a.cust_id=b.id JOIN payments c ON a.id=c.acc_id WHERE c.amount>1000 AND b.region='EMEA' ORDER BY c.amount DESC;

Fix

SELECT
    a.id,
    b.name,
    c.amount
FROM accounts AS a
JOIN customers AS b
    ON a.cust_id = b.id
JOIN payments AS c
    ON a.id = c.acc_id
WHERE
    c.amount > 1000
    AND b.region = 'EMEA'
ORDER BY
    c.amount DESC;

Benefits:

• Easier to spot missing commas or misplaced keywords.
• Simplifies peer review and future maintenance.

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5. Incorrect Clause Order

Why it happens

SQL follows a strict order: SELECT → FROM → WHERE → GROUP BY → HAVING → ORDER BY → LIMIT. Swapping clauses leads to syntax errors or unexpected results.

Example (error)

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

Fix

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

Tip: Memorise the order as “S‑F‑W‑G‑H‑O‑L” (Select, From, Where, Group, Having, Order, Limit).

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6. Improper Use of Aliases

Why it happens

Aliases are meant to make queries shorter, but ambiguous or missing aliases cause confusion, especially when the same table appears multiple times.

Example (error)

SELECT id, name
FROM employees e1
JOIN employees e2 ON e1.manager_id = e2.id
WHERE name = 'Alice';

Which name column does the WHERE refer to? The query may return unexpected rows.

Fix

SELECT e1.id, e1.name, e2.name AS manager_name
FROM employees AS e1
JOIN employees AS e2
    ON e1.manager_id = e2.id
WHERE e1.name = 'Alice';

Best practice: Always qualify column names with the appropriate alias when more than one table is in scope.

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7. Syntax Errors: Unmatched Parentheses or Missing Keywords

Why it happens

Functions like COUNT(), SUM(), or CASE require matching parentheses. A missing closing parenthesis leads to cryptic errors.

Example (error)

SELECT COUNT(* FROM sales;

Fix

SELECT COUNT(*)
FROM sales;

Tip: Many editors highlight matching parentheses. If you’re writing complex expressions, break them onto separate lines.

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8. Incorrect Join Logic

Why it happens

Using the wrong join type or forgetting the ON clause can produce Cartesian products (every row matched with every other row), inflating result sets dramatically.

Example (error)

SELECT *
FROM customers
JOIN orders;   -- missing ON

If customers has 10 000 rows and orders has 20 000 rows, the result set becomes 200 million rows.

Fix

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

Additional advice:

• Prefer explicit INNER JOIN, LEFT JOIN, etc., over the older comma‑style joins.
• When filtering after a join, place conditions on the joined table in the ON clause if they affect the join cardinality.

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9. Mishandling NULL Values

Why it happens

NULL is not the same as an empty string or zero. Using = or != with NULL always yields UNKNOWN, which filters the row out of the result set.

Example (error)

SELECT *
FROM users
WHERE last_login != NULL;   -- always false

Fix

SELECT *
FROM users
WHERE last_login IS NOT NULL;

Tips:

• Use COALESCE(column, default_value) to replace NULL with a sensible default in calculations.
• Remember that aggregate functions (SUM, AVG) ignore NULL automatically, but COUNT(column) does not count NULLs—use COUNT(*) if you need a total row count.

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10. Assuming Data Quality (Duplicates, Bad Formats)

Why it happens

Analysts often trust that source tables are clean. Duplicate rows, inconsistent date formats, or stray whitespace can skew aggregates.

Example (error)

SELECT COUNT(DISTINCT order_id) AS unique_orders,
       SUM(total_amount) AS revenue
FROM orders;

If order_id contains leading zeros in some rows ('00123' vs '123'), duplicates aren’t detected.

Fix

SELECT COUNT(DISTINCT TRIM(LEADING '0' FROM order_id)) AS unique_orders,
       SUM(total_amount) AS revenue
FROM orders;

General strategy:

Step	Action
1	Run a data profiling query (SELECT column, COUNT(*) FROM table GROUP BY column HAVING COUNT(*) > 1).
2	Use TRIM, UPPER, LOWER, or CAST to normalise formats.
3	Apply unique constraints or primary keys where possible.
4	Document data‑quality assumptions in a README or data‑dictionary.

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Bonus: Performance‑Related Mistakes to Watch

While the ten items above focus on correctness, two common performance pitfalls often accompany them:

1. Selecting * in production queries – pulls unnecessary columns, increasing I/O.
   Fix: Explicitly list required columns.

2. Not using indexes for filter columns – leads to full table scans.
   Fix: Work with DBAs to add appropriate indexes, or use EXPLAIN to verify query plans.

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Conclusion

SQL is deceptively simple; a tiny oversight can cascade into hours of wasted time and inaccurate insights. By recognising the top 10 mistakes—from missing commas to mishandling NULLs—and applying the fixes outlined here, you’ll write queries that are:

• Correct – no hidden syntax or logic errors.
• Readable – easy for teammates (and future you) to understand.
• Performant – leaner data pulls and faster results.

Make these habits part of your regular workflow, and you’ll spend more time analysing data and less time debugging it. Happy querying!