# Spreadsheets → SQL → Pipelines

You started in a spreadsheet. Almost everyone does. Then one day the file got slow, or a teammate
overwrote your numbers, or you realized you'd been hand-copying the same report every Monday for a year.
That nagging feeling - *there has to be a better way to do this* - is real, and it's the moment your
data work is ready to grow.

This guide walks the path most data work actually travels: **spreadsheet → SQL → pipeline.** Three
stages, each solving a specific pain the stage before it couldn't. You don't skip ahead because a tool
is fancier; you move up only when the work outgrows where it lives. By the end you'll be able to look at
a messy data task and say, calmly, "this belongs in a spreadsheet" - or "this needs a database now" - or
"this has to become a pipeline."

## How to read this

- **Trying to decide what tool fits a specific task?** Skim each phase's opening - every phase names the
  exact pain that signals "time to move up."
- **Want it to finally make sense?** Read in order. Each stage is built on the one before, and the whole
  point is seeing *why* you move, not just *that* you do.

## The phases

1. **[Where Everyone Starts: Spreadsheets](01-where-everyone-starts-spreadsheets.md)** - why spreadsheets
   are genuinely great, and the exact places they quietly break.
2. **[Outgrowing the Sheet: SQL & Databases](02-outgrowing-the-sheet-sql-and-databases.md)** - when one
   shared source of truth, real types, and millions of rows mean it's time to graduate.
3. **[When It Has to Run Itself: Pipelines](03-when-it-has-to-run-itself-pipelines.md)** - when the work
   must be automated, scheduled, and repeatable, you build a pipeline - and what that buys and costs.

> This guide is about *when* to move and *why*. The deep mechanics of building pipelines live in their
> own guide: [ETL & ELT Pipelines](/guides/etl-elt-pipelines). And the broader field this all rolls up
> into is covered in [What Is Data Engineering](/guides/what-is-data-engineering).