SQL vs Excel: When to Use Each (With Side-by-Side Examples)

Example 1: Total Sales by Region

=SUMIF(A:A, "West", C:C)

SELECT region, SUM(amount) as total
FROM orders
GROUP BY region;

Example 2: Look Up Customer Name for an Order

=VLOOKUP(A2, Customers!A:B, 2, FALSE)

SELECT o.order_id, c.customer_name
FROM orders o
JOIN customers c 
  ON o.customer_id = c.customer_id;

Example 3: Count Orders Above $1,000

=COUNTIF(C:C, ">1000")

SELECT COUNT(*) as high_value_orders
FROM orders
WHERE amount > 1000;

Example 4: Combine Data from Two Sources

1. Open Power Query
2. Import orders.csv
3. Import customers.csv  
4. Merge Queries
5. Select matching columns
6. Expand merged data
7. Close & Load

SELECT o.*, c.customer_name, c.email
FROM orders o
JOIN customers c 
  ON o.customer_id = c.customer_id;

Excel Wins When:

• 1. Quick ad-hoc exploration. You have 500 rows and need to understand the data in 10 minutes. Excel's point-and-click interface is faster than writing queries.
• 2. Creating visualizations fast. Need a bar chart for a meeting in an hour? Excel's charting is immediate. SQL produces data, not visuals.
• 3. Sharing with non-technical stakeholders. Your CFO knows how to open an .xlsx file. They don't know how to run SQL queries.
• 4. One-off calculations. If you'll never run this analysis again, the overhead of writing SQL may not be worth it.
• 5. Data entry and manual adjustments. Need to fix 20 values by hand? Excel is designed for manual manipulation. SQL is designed to prevent it.

SQL Wins When:

• 1. Dataset exceeds ~100k rows. Excel starts lagging. Formulas recalculate slowly. SQL handles it without blinking.
• 2. Combining data from multiple tables. Three JOINs in SQL vs three nested VLOOKUPs in Excel. SQL is cleaner and faster.
• 3. Analysis needs to be repeated. Save the query, run it monthly. No manual steps to remember.
• 4. Data integrity matters. SQL enforces types, constraints, and relationships. Excel lets you put anything anywhere.
• 5. Multiple people need the same data. SQL is the single source of truth. Excel is 47 versions of “final_report_v3_FINAL_ACTUAL.xlsx”.

The Multi-Table JOIN Excel Can't Do Cleanly:

SELECT 
  c.customer_name,
  o.order_date,
  p.product_name,
  o.quantity,
  o.quantity * p.price as total
FROM orders o
JOIN customers c ON o.customer_id = c.customer_id
JOIN products p ON o.product_id = p.product_id
WHERE o.order_date >= '2024-01-01'
ORDER BY total DESC;

This single query pulls customer names, order details, and product info from three tables. In Excel, you'd need nested VLOOKUPs or Power Query wizardry.

Real Workflow Example:

-- SQL: Summarize 5 million rows into 12 monthly totals
SELECT 
  DATE_TRUNC('month', order_date) as month,
  SUM(amount) as total_sales,
  COUNT(*) as order_count
FROM orders
WHERE order_date >= '2024-01-01'
GROUP BY DATE_TRUNC('month', order_date)
ORDER BY month;

This query turns 5 million rows into 12 rows. Export to Excel, add a line chart, format for the board meeting. Best of both worlds.

The 5 Essential SQL Queries for Excel Users:

1. SELECT = Choosing columns

SELECT customer_name, email FROM customers;

2. WHERE = Filtering rows (like Excel filters)

SELECT * FROM orders WHERE amount > 1000;

3. ORDER BY = Sorting

SELECT * FROM orders ORDER BY order_date DESC;

4. GROUP BY + SUM = Pivot tables

SELECT region, SUM(amount) FROM orders GROUP BY region;

5. JOIN = VLOOKUP across sheets

SELECT o.*, c.name 
FROM orders o 
JOIN customers c ON o.customer_id = c.id;