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Connect, Query, Excel: Demystifying Relational Databases

July 27, 2023

Data is the lifeblood of modern businesses. Whether you are managing customer records, tracking inventory, or analyzing financial transactions, organizing, and retrieving data efficiently is crucial.

But here is the problem handling large datasets in spreadsheets alone leads to slow performance, errors, and poor collaboration. This is where relational databases (RDBMS) come into play.

A relational database organizes data into structured tables that connect via relationships, ensuring better accuracy, scalability, and security. And when combined with Microsoft Excel, businesses can query, analyze, and visualize their data effortlessly.

So, how do you connect Excel to a database, and what role does SQL play in querying data? Let us explore.

What is a Relational Database?

A relational database is a structured system where data is stored in tables (relations) consisting of:

Rows (Records) – Individual entries

Columns (Fields) – Specific attributes of data

Keys – Unique identifiers that create relationships between tables

For example, an e-commerce business may have:

Customers & Orders Tables

Customers Table

Field	Data Type	Description
CustomerID	Primary Key	Unique identifier for each customer
Name	VARCHAR	Customer’s full name
Email	VARCHAR	Customer’s email address

Orders Table

Field	Data Type	Description
OrderID	Primary Key	Unique identifier for each order
CustomerID	Foreign Key	References CustomerID in Customers Table
Order Date	DATE	Date when the order was placed

Since the CustomerID exists in both tables, you can query customer purchase history efficiently.

Why Businesses Use Relational Databases

Data integrity – Eliminates duplicate and inconsistent data

Scalability – Handles millions of records without slowing down

Multi-user access – Teams can work on the same database simultaneously

Data security – Role-based access control ensures only authorized personnel can view or edit data

Popular Relational Database Management Systems (RDBMS)

1. MySQL

Open-source and widely used for web applications

Ideal for startups and small businesses

2. PostgreSQL

Enterprise-grade performance with strong data integrity
Used in finance, government, and large applications

3. Microsoft SQL Server

A powerful database solution for enterprises

Seamless integration with Excel, Power BI, and business analytics tools

4. Oracle Database

Best for large-scale applications (banking, ERP, government databases)

Highly secure and reliable

5. SQLite

A lightweight database used in mobile and embedded applications

No separate database server needed

Why Use Excel for Connecting to Relational Databases?

Excel remains a go-to tool for data analysis, but when working with large datasets,managing data manually is not practical.

Benefits of Connecting Excel to a Relational Database

Live data updates – Query real-time information

Efficient analysis – Use pivot tables, charts, and Power Query
Reduce file size – Instead of storing data in Excel, fetch only what is needed

Avoid duplication – Pull structured, accurate data directly from databases

Limitations of Excel

Not built for large-scale databases

Performance slows down with millions of rows

No built-in relational structure like databases

How to Connect Excel to a Relational Database

Step 1: Set Up Your Database Connection

Ensure your database allows remote access and you have the correct user credentials.

Step 2: Configure an ODBC Connection in Excel

Go to Control Panel > Administrative Tools > ODBC Data Sources
Click Add and select your database driver
Enter the server details, username, and password

Step 3: Use Microsoft Query to Retrieve Data

Open Excel
Go to Data > Get Data > From Other Sources > From ODBC
Select the ODBC connection you just created
Choose the tables or write an SQL query

Step 4: Import and Analyze Data in Excel

Once your data is imported, you can:

Filter and sort records:

Create pivot tables and charts

Perform advanced calculations

Writing SQL Queries in Excel

SQL (Structured Query Language) is how we communicate with relational databases. Instead of manually copying and pasting data, you can use SQL to retrieve exactly what you need.

Basic SQL Queries in Excel

1. Retrieve All Records

SQL Code Box

SQL Query

SELECT * FROM Customers;

Result: Displays all customer records.

2. Filter Data with a WHERE Clause

SQL Code Box

SQL Query

SELECT * FROM Orders WHERE OrderDate >= '2024-01-01';

Result: Returns orders placed after January 1, 2024.

3. Join Multiple Tables

SQL Code Box

SQL Query

SELECT Customers.Name, Orders.OrderID, Orders.TotalAmount  
FROM Customers  
JOIN Orders ON Customers.CustomerID = Orders.CustomerID;

Result: Displays customer names alongside their orders and total amount spent.

Best Practices for Working with Databases in Excel

Optimize queries – Fetch only the required columns and rows

Reduce data load – Avoid retrieving millions of records at once

Use filters – Apply WHERE conditions to limit dataset size

Enable security – Store credentials securely and restrict access

Comparing Relational Databases vs. NoSQL Databases

SQL vs. NoSQL Databases

Feature	Relational Databases (SQL)	NoSQL Databases
Data Structure	Tables with fixed schema	Flexible schema (documents, key-value, etc.)
Scalability	Vertical scaling	Horizontal scaling
Best Use Cases	Finance, CRM, ERP	Big data, social media, IoT

For structured data and transactions, SQL databases are ideal. If you are handling unstructured data like social media feeds, go with NoSQL.

Conclusion: The Power of Relational Databases in Excel

Relational databases remain essential for businesses, ensuring data accuracy, efficiency, and security. When integrated with Excel, they unlock powerful features like live data retrieval, advanced queries, and seamless analysis.

By leveraging SQL queries and database connections, you can automate workflows, enhance decision-making, and manage large datasets effortlessly.