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Why Learn SQL? From Spreadsheets to Databases

•5 min read
sql

For years, spreadsheets and out of the box tools like Power Query have served as my go-to solutions for organizing, cleaning, and analyzing data. They’re flexible, visual, and incredibly familiar. But as datasets grew larger and my work grew more complex, I began to encounter real limitations—slow performance, difficulty maintaining data integrity, and challenges sharing work at scale.

That’s what led me to SQL.

Structured Query Language (SQL, often pronounced “sequel”) is the backbone of modern data management. It powers everything from customer databases to e-commerce platforms, financial systems, and even the dashboards we use to track performance. While tools like Excel and Power Query are excellent for one-off or mid-sized tasks, SQL gives us power, precision, and scalability.

In this post, I’ll share why I’m transitioning into SQL (specifically Microsoft SQL Server), what advantages it offers, and how this series will help other spreadsheet-savvy professionals make the leap.

Why Move Beyond Excel?

Let’s start with what Excel does well:

  • Exploration and prototyping: PivotTables, filters, and formulas make it easy to explore data quickly.
  • Data cleaning: With Power Query, you can perform robust transformations without code.
  • Reporting: Tables, charts, and conditional formatting make Excel a decent reporting tool.

But as we push Excel beyond its sweet spot, cracks begin to show.

Common Limitations of Excel:

  • Performance: Files slow down as row counts exceed 50,000+. Memory usage balloons with complex formulas or links across sheets.
  • Data integrity: Anyone can accidentally overwrite a value. Cell-based logic is hard to audit at scale.
  • Collaboration: Version control is painful, and Excel isn't designed for multi-user concurrent access (this is improving with cloud hosted files in SharePoint, but I have not found this painless).
  • Scalability: Combining large datasets becomes clunky and unreliable.

In contrast, SQL is built for scale, clarity, and repeatability.

Why SQL (and Why Microsoft SQL Server)?

SQL is the standard language for managing relational databases—systems that store data in structured tables with defined relationships. It allows you to:

  • Query millions of rows in seconds
  • Join data from multiple tables with ease
  • Write logic that is reusable, auditable, and version-controllable
  • Automate processes that would take dozens of steps in a spreadsheet

Why I Chose Microsoft SQL Server:

While there are many relational databases out there (PostgreSQL, MySQL, SQLite, etc.), I chose Microsoft SQL Server for several reasons:

  • It’s widely used in enterprise environments
  • The ecosystem includes powerful tools like SQL Server Management Studio (SSMS)
  • It offers full support for Transact-SQL (T-SQL), Microsoft’s extended SQL dialect
  • Excellent documentation and integration with other Microsoft tools (e.g. Power BI, Azure)

The Declarative Power of SQL

One of the first things I learned about SQL is that it’s declarative, not procedural.

In Excel or Python, you often tell the computer how to perform a task step by step. In SQL, you simply describe what you want, and let the database engine figure out how to get it.

For example:

SELECT FirstName, LastName
FROM Customers
WHERE Country = 'Canada';

This tells the database:

  • Give me the FirstName and LastName
  • From the Customers table
  • Where the Country is Canada

That’s it. Clean, readable, and powerful. No loops, no formulas in hidden cells.

What This Blog Series Will Cover

I’ve structured this series for readers who are completely new to SQL, but who likely already work with data in some form. If you’re an Excel user, data analyst, operations manager, or just a curious builder—this series is for you.

Here’s what we’ll do together:

  • Set up a SQL Server environment and create your first tables
  • Learn the core SQL commands: SELECT, WHERE, JOIN, GROUP BY, etc.
  • Write subqueries and build views to simplify recurring work
  • Understand indexing and performance basics
  • Learn how to structure queries cleanly and avoid common mistakes

Each post will focus on one concept, with simple explanations, real-life analogies, and runnable examples. I’ll also point out differences between SQL Server and other databases (like PostgreSQL or MySQL) along the way.

Final Thoughts

Learning SQL felt intimidating at first—it seemed like another language entirely. But with a little practice, it’s become one of the most rewarding skills I’ve added to my toolkit. The clarity of SQL logic, the speed of data exploration, and the ability to automate analysis at scale have completely changed how I approach data problems.

If you’ve ever felt the limits of Excel, or found yourself building the same Power Query chains again and again, I encourage you to follow along. SQL won’t replace those tools entirely—but it will give you more control, better performance, and a deeper understanding of how your data truly works.