The Concept and Basic Use of SQL (Query Language)#

🌱 Foundations 🧰 Tools, Applications & Ethics Lesson 022

Important

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Asking questions of databases#

Organisational data mostly does not live in spreadsheets; it lives in databases — structured collections of tables holding millions of records. SQL (Structured Query Language) is how you talk to them: a language for describing the data you want and letting the database fetch it. Where a spreadsheet shows you everything and lets you point, SQL lets you ask — and the asking scales to sizes no grid could display.

The core idea: declare, don’t fetch-and-loop#

An SQL query states what you want, not how to find it:

SELECT customer_id, order_date, amount
FROM   orders
WHERE  amount > 100;

Read it as a sentence: select these columns, from this table, where this condition holds. The three clauses are the irreducible core:

SELECT — which columns (attributes) to return; SELECT * means all.
FROM — which table the data lives in.
WHERE — which rows qualify, via conditions (=, >, <, combined with AND / OR).

The database engine — not you — figures out the efficient way to satisfy the request across millions of rows, which is precisely why the language stays this simple at this scale.

The spreadsheet translation#

Every clause has a spreadsheet counterpart you already know: SELECT is choosing columns to look at, FROM is opening the right sheet, WHERE is a filter. The difference is not conceptual but operational: the query is text — repeatable, shareable, reviewable — and it runs against the live, shared source of record rather than a private copy. When the analysis section later adds grouping and joining, those too will be filters and pivots you have already met, in query form.

Why every analyst learns it#

Three durable reasons. Location: the data you need is already in a database; SQL goes to it rather than exporting fragile copies. Scale: a WHERE over ten million rows returns in seconds. Ubiquity: SQL has been the standard interface to relational data for decades, is asked for in more analyst job postings than any other technical skill, and transfers almost unchanged across database products.

The caveat#

A query answers exactly what it says, which is not always what you meant — WHERE amount > 100 silently excludes the row where amount is missing, and no error will tell you so. SQL’s precision is a feature that demands the same precision of your question; the deeper query techniques and their pitfalls get full treatment in the cleaning and analysis sections.

Hint