Overview
Data is recorded information. Structured data lives in tables with rows and columns. Unstructured data is everything else.
On this page7 sections
The idea
Data is recorded information. Every time someone places an order, clicks a button, makes a payment, or sends a message, that action can be recorded as data. A single order might include the customer name, the product purchased, the price, and the date. Each of those pieces is a data point.
Computers store data in different formats. The most common format for business data is structured data: information organized into rows and columns, like a spreadsheet. Each row represents one record (one order, one customer, one payment). Each column represents one property (name, price, date, status).
Unstructured data does not fit neatly into columns: images, videos, chat logs, emails. Data engineers often convert unstructured data into structured tables so that analysts can query it with SQL.
Why this exists
Imagine you run an online store. Every day, hundreds of customers visit your website, browse products, add items to their carts, and make purchases. All of that activity generates data. But the data is scattered: website clicks are in one system, payments are in another, shipping information is in a third.
Without organizing this data into structured tables, you cannot answer basic questions: How many orders did we get today? What was our revenue? Which products are selling best? Data engineering starts with understanding what data is and how to structure it.
Picture this
Structured data fits in tables. Unstructured data needs processing before it can be queried.
Most data engineering work involves structured and semi-structured data.
| Data type | Example | Storage format | Can query with SQL? |
|---|---|---|---|
| Structured | Order records, payments | Database tables | Yes, directly |
| Semi-structured | JSON from APIs, XML feeds | Files or document stores | After parsing |
| Unstructured | Images, videos, free text | Object storage (files) | After extraction |
The SQL editor in this tab already holds structured tables with realistic e-commerce data: orders, payments, products, customer events, and more. Throughout this track, you will learn to query these tables to answer business questions.
A small example
Here is a tiny slice of what an orders table looks like. One row is one order. One column is one property of that order.
This is structured data. SQL can filter, sum, and join it.
| order_id | product | price | status |
|---|---|---|---|
| ORD-001 | laptop | 999.99 | paid |
| ORD-002 | mouse | 24.99 | cancelled |
| ORD-003 | keyboard | 79.99 | paid |
If the same three orders lived only in email receipts, you would have to read each email by hand. That is unstructured data. Data engineers pull those facts into a table first.
Data is everywhere
Every app, website, and device generates data. Data engineering is about making that data usable.
Copy-paste without reading the output
Run Sample first. If the numbers or row count look wrong, stop and re-read the previous section before changing code.
Common beginner questions
What is the difference between data and information?
Data is raw recorded facts (order_id: ORD-001, price: 49.99). Information is data that has been processed to answer a question (total revenue last month: $12,450). Data engineers build the systems that turn data into information.
Why not just use spreadsheets?
Spreadsheets work for small datasets (hundreds or thousands of rows). When you have millions or billions of rows, you need a database and SQL. Databases are faster, support multiple users, and enforce data quality rules that spreadsheets cannot.
What comes next
In the next lesson, you will learn what a database is and why it exists. Then you will explore the tables available in this SQL editor.
Practice
Run Sample to see the first 5 rows of the orders table. Notice how each row is one order and each column is one property of that order.