The Struggle with Large SQL Queries

If you’ve worked with SQL for any meaningful length of time, you’ve probably faced the frustration of reading or writing a massive query. Maybe it had so many subqueries that you got lost in the parentheses. Or maybe it had deeply nested logic that made debugging feel impossible. For business analysts, data engineers, and developers alike, large SQL queries can quickly become overwhelming.

This is exactly where CTEs’ Common Table Expressions come to the rescue.

Think of a CTE as a temporary result set you can reference within your query. It’s like creating a mini, virtual table that simplifies the logic of complex queries and makes your SQL far more readable and maintainable. In short, CTEs are not just a nice-to-have; they’re a game-changer for anyone who works with data.

In this blog post, we’ll explore what CTEs are, why they matter, how they work, and how analysts can use them to solve real-world problems. By the end, you’ll understand why mastering CTEs is one of the most valuable skills you can add to your SQL toolkit.

What Exactly Are CTEs?

A Common Table Expression (CTE) is a temporary, named result set defined within the execution scope of a single SQL statement. Unlike permanent tables or views, a CTE only exists for the duration of the query that uses it.

The syntax is simple:

WITH cte_name AS (

    SELECT column1, column2

    FROM table_name

    WHERE condition

)

SELECT *

FROM cte_name;

Here’s what’s happening:

1. You define a CTE using the WITH keyword.
2. Inside the parentheses, you write a query that generates your result set.
3. You can then use the CTE as if it were a table in your main query.

In practice, this means you can break down a complex problem into smaller, understandable parts.

Why Business Analysts Should Care About CTEs

Business analysts are often stuck between two competing realities:

• Stakeholders need insights quickly.
• Databases are messy, with multiple tables, joins, and conditional logic.

Without CTEs, analysts often resort to subqueries. While subqueries are powerful, they can be difficult to read, debug, and scale. With CTEs, however, analysts can:

• Make queries more readable: You can label and organize intermediate steps.
• Reuse logic: Instead of repeating the same subquery, you define it once and use it multiple times.
• Simplify debugging: By isolating parts of the query, errors are easier to detect.
• Handle recursion: Some business problems (like hierarchical org charts or bill of materials) require recursive queries, which are made possible by CTEs.

The Problem with Subqueries

Before we dive deeper, let’s examine why CTEs are so important by comparing them to subqueries.

Suppose you want to find the top 5 customers by total spending:

SELECT customer_id, total_spent

FROM (

    SELECT customer_id, SUM(amount) AS total_spent

    FROM orders

    GROUP BY customer_id

) AS sub

ORDER BY total_spent DESC

LIMIT 5;

This works, but imagine if you needed to reuse the same customer spending calculation across multiple queries. You’d have to rewrite it each time, leading to duplication and potential errors.

With a CTE, the logic becomes cleaner:

WITH customer_totals AS (

    SELECT customer_id, SUM(amount) AS total_spent

    FROM orders

    GROUP BY customer_id

)

SELECT customer_id, total_spent

FROM customer_totals

ORDER BY total_spent DESC

LIMIT 5;

Readable. Reusable. Debuggable.

Breaking Down Complex Queries into Steps

One of the biggest strengths of CTEs is their ability to break down complex problems into sequential steps.

For example, let’s say you’re analyzing employee performance and need to:

1. Calculate each employee’s total sales.
2. Find the average sales per department.
3. Compare employees to their department average.

Without CTEs, you’d end up nesting queries within queries. With CTEs, the logic flows naturally:

WITH employee_sales AS (

    SELECT employee_id, department_id, SUM(sales_amount) AS total_sales

    FROM sales

    GROUP BY employee_id, department_id

),

department_avg AS (

    SELECT department_id, AVG(total_sales) AS avg_sales

    FROM employee_sales

    GROUP BY department_id

)

SELECT e.employee_id, e.total_sales, d.avg_sales,

       (e.total_sales – d.avg_sales) AS variance_from_avg

FROM employee_sales e

JOIN department_avg d ON e.department_id = d.department_id;

This query reads almost like plain English. Each step builds on the last, making it easier to understand and explain to others.

Real-World Use Cases of CTEs for Analysts

1. Customer Segmentation

Imagine you want to segment customers into high, medium, and low spenders.

WITH customer_totals AS (

    SELECT customer_id, SUM(amount) AS total_spent

    FROM orders

    GROUP BY customer_id

)

SELECT customer_id,

       CASE

           WHEN total_spent > 1000 THEN ‘High Spender’

           WHEN total_spent BETWEEN 500 AND 1000 THEN ‘Medium Spender’

           ELSE ‘Low Spender’

       END AS segment

FROM customer_totals;

This helps marketing teams design campaigns tailored to each segment.

2. Employee Retention Analysis

Suppose HR wants to analyze employee churn by department.

WITH hires AS (

    SELECT department_id, COUNT(*) AS total_hires

    FROM employees

    WHERE hire_date >= ‘2020-01-01’

    GROUP BY department_id

),

terminations AS (

    SELECT department_id, COUNT(*) AS total_terminations

    FROM employees

    WHERE termination_date IS NOT NULL

    GROUP BY department_id

)

SELECT h.department_id, h.total_hires, t.total_terminations,

       (CAST(t.total_terminations AS FLOAT) / h.total_hires) * 100 AS churn_rate

FROM hires h

JOIN terminations t ON h.department_id = t.department_id;

This gives HR a clear picture of where turnover is highest.

3. Recursion: Organizational Hierarchies

Recursive CTEs allow you to model hierarchies such as managers and subordinates.

WITH RECURSIVE org_chart AS (

    SELECT employee_id, manager_id, 1 AS level

    FROM employees

    WHERE manager_id IS NULL

    UNION ALL

    SELECT e.employee_id, e.manager_id, oc.level + 1

    FROM employees e

    INNER JOIN org_chart oc ON e.manager_id = oc.employee_id

)

SELECT *

FROM org_chart;

This makes it possible to visualize reporting structures or bill-of-material relationships.

Best Practices for Using CTEs

1. Use meaningful names: Give CTEs descriptive names like monthly_sales or customer_segments.
2. Limit nesting: CTEs simplify queries, but stacking too many can hurt readability.
3. Check performance: CTEs don’t always improve performance they improve readability. For performance optimization, indexes or temp tables may still be required.
4. Reuse logic: If you find yourself copying a subquery multiple times, replace it with a CTE.
5. Document your steps: Treat CTEs like stepping stones; add comments to explain why each one exists.

CTEs vs Views vs Temp Tables

Many analysts wonder: if CTEs are so useful, how do they differ from views and temporary tables?

• CTEs: Exist only within a single query. Great for readability and modular design.
• Views: Saved queries stored in the database. Great for reusability across analysts, but requires database permissions.
• Temp Tables: Physical tables stored temporarily. Useful for very large datasets that need indexing.

CTEs are the best choice when you need one-time simplification of a complex query.

Common Mistakes Analysts Make with CTEs

1. Assuming performance improvement: CTEs don’t always make queries faster. They’re about clarity.
2. Overusing recursion: Recursive CTEs are powerful, but they can spiral into inefficiency if not carefully controlled.
3. Naming poorly: Using generic names like cte1 or cte2 reduces readability, the exact opposite of their purpose.
4. Ignoring alternatives: For repeated, company-wide needs, views, or materialized views may be better.

How CTEs Transform Your Workflow

By adopting CTEs into your SQL practice, you’ll notice:

• You write cleaner queries that colleagues can easily understand.
• Debugging becomes easier because logic is broken into parts.
• Business problems become easier to solve because queries mirror the way you naturally think through analysis.

Most importantly, using CTEs elevates you from being a “query writer” to being a true data problem-solver.

 CTEs as a Superpower for Analysts

At first glance, a CTE might seem like a small feature in SQL. But once you start using it, you’ll realize it’s much more than that; it’s a superpower for simplifying large queries, improving readability, and aligning your SQL logic with the way humans think.

Related Article: Why SQL Skills Are a Superpower for Business Analysts

For business analysts, who often need to explain queries to non-technical stakeholders, CTEs are invaluable. They turn intimidating walls of SQL into step-by-step stories that make sense.

So, the next time you find yourself writing a query that feels too complex, pause and ask: Can I break this down into smaller steps with a CTE? Chances are, the answer is yes, and it will save you hours of frustration.