Tuesday, April 14, 2026
Volume 1.3
All posts
Lv.1 IntroPostgreSQL
10 min readLv.1 Intro

What Is PostgreSQL? A 10-Minute Beginner Guide

What Is PostgreSQL? A 10-Minute Beginner Guide

New to databases? This guide explains what PostgreSQL is, why teams pick it for web and analytics workloads, and how tables, rows, and columns fit together—without assuming prior DBA experience. You get a short, readable SELECT example and a clear path to what to study next, so the official docs feel approachable instead of overwhelming.

If you are new to databases, that is fine. This single article helps you see the big picture of PostgreSQL.

Table of contents

  1. What is a database?
  2. What is PostgreSQL?
  3. PostgreSQL history at a glance
  4. Why use PostgreSQL?
  5. Comparing databases
  6. Core ideas — tables, rows, columns
  7. A taste of basic SQL
  8. Installing PostgreSQL
  9. First steps — hands-on example
  10. What to study next

1. What is a database?

A database is, in simple terms, a structured place to store data.

Think of a spreadsheet: rows and columns where you stash information and look things up quickly. A database works on a similar idea, but it is built to be much faster and safer at large scale.

  • Store — keep members, orders, posts, and so on in an organized way
  • Search — find what you need even when there are millions of rows
  • Update / delete — change or remove data reliably

2. What is PostgreSQL?

PostgreSQL (often shortened to Postgres) is an open-source relational database management system (RDBMS).

Relational means you store data in tables and you can define relationships between those tables.

It is free to use yet offers features many teams expect from enterprise databases, so it shows up everywhere from startups to large companies.

3. PostgreSQL history at a glance

YearMilestone
1986POSTGRES project starts at UC Berkeley
1996SQL support and naming evolve (via Postgres95 and related steps toward today’s PostgreSQL)
2000sFast growth driven by the open-source community
TodayAmong the most widely used open-source databases

4. Why use PostgreSQL?

Free and open source

No license fee for typical use, and you can use it commercially under its license.

Strong standard SQL

PostgreSQL tracks SQL standards closely, so what you learn transfers well to other databases.

Extensibility

Beyond built-in types like JSON and arrays, you can add capabilities through extensions. Geospatial work often uses PostGIS.

Reliability

ACID transactions help you reason about consistency and safety. For concepts and syntax, see the official transaction tutorial.

Active community

Documentation, tutorials, and community answers are easy to find.

5. Comparing databases

TopicPostgreSQLMySQLSQLite
Open sourceYesYesYes
Advanced SQLStrongGoodBasic
JSONStrongPartialLimited
Large workloadsExcellentVery goodSmall / embedded
Learning curveMediumEasierEasiest
Typical usesWeb, analytics, enterpriseWebMobile, embedded

These are broad tendencies; real behavior depends on versions and how you run them.

6. Core ideas — tables, rows, columns

In PostgreSQL, data lives in tables.

users table — columns and primary key (PK)

[ users table ]

id  | name   | email               | age
----|--------|---------------------|----
 1  | Alice  | alice@example.com   | 28
 2  | Bob    | bob@example.com     | 34
 3  | Carol  | carol@example.com   | 22
  • Table — the whole grid
  • Row — one record (e.g. one user)
  • Column — a field (e.g. name, email)
  • Primary key — a value that uniquely identifies a row (often id)

7. A taste of basic SQL

You work with PostgreSQL using SQL (Structured Query Language).

Reading data (SELECT)

-- all rows in users
SELECT * FROM users;

-- only name and email
SELECT name, email FROM users;

-- age 30 or older
SELECT * FROM users WHERE age >= 30;

Inserting rows (INSERT)

INSERT INTO users (name, email, age)
VALUES ('Dan', 'dan@example.com', 25);

Updating rows (UPDATE)

UPDATE users
SET age = 29
WHERE name = 'Alice';

Deleting rows (DELETE)

DELETE FROM users
WHERE id = 3;

8. Installing PostgreSQL

macOS

brew install postgresql@16
brew services start postgresql@16

Windows

  1. Download the installer from PostgreSQL for Windows.
  2. Run the wizard with mostly default options.
  3. Installers from EDB (and similar) often offer pgAdmin as an optional add-on—not always selected by default.

Linux (Ubuntu / Debian)

sudo apt update
sudo apt install postgresql postgresql-contrib
sudo systemctl start postgresql

Distro packages can lag behind the latest major version. For newer releases, follow PostgreSQL downloads for Ubuntu / PGDG.

Check the install

psql --version
# e.g. psql (PostgreSQL) 16.x

9. First steps — hands-on example

Create a database and table

CREATE DATABASE myapp;

CREATE TABLE users (
    id    SERIAL PRIMARY KEY,
    name  VARCHAR(50) NOT NULL,
    email VARCHAR(100) UNIQUE NOT NULL,
    age   INT,
    created_at TIMESTAMP DEFAULT NOW()
);

INSERT INTO users (name, email, age)
VALUES
    ('Alice', 'alice@example.com', 28),
    ('Bob', 'bob@example.com', 34);

SELECT * FROM users;

Tip: SERIAL builds an auto-incrementing integer id so you do not assign it by hand. Since PostgreSQL 10, many teams also prefer standard SQL GENERATED { ALWAYS | BY DEFAULT } AS IDENTITY. See the docs on numeric types and SERIAL.

10. What to study next

Official docs

Practice sites

GUI tools

  • pgAdmin — official GUI
  • DBeaver — free multi-database client
  • TablePlus — polished UI (paid / free tiers)

Concepts to tackle next

  • JOIN — combine tables
  • Index — speed up lookups
  • Transaction — safer reads and writes
  • View — save recurring queries

References (official)

Sources used when checking technical claims in this article.


I hope this guide is a useful first step into PostgreSQL. When you get stuck, pair the official docs with community threads—you will rarely be the first person to hit that question.

Share This Article

Recommended Reads

Why PostgreSQL? Part 5 — The ecosystem: pgvector, PostGIS, TimescaleDB

One more PostgreSQL extension—and you can seriously discuss vector search, geospatial queries, time-series analytics, and BM25-style full-text search on the same engine. This series finale walks through pgvector, PostGIS, TimescaleDB, and ParadeDB (pg_search): what public benchmarks and vendor write-ups claim, where “replace the specialist” is conditionally true, and how to read latency/cost numbers when managed services, self-hosting, and tuning assumptions differ. It closes the five-part arc on why PostgreSQL is often the lowest-regret default—reliability, extensibility, ecosystem depth—and when a separate system still earns its place. Use the decision inputs at the end alongside the comparison table: growth rate, staffing, failure tolerance, compliance, and how you define TCO.

Read

Why PostgreSQL? Part 4 — MongoDB & Oracle: real migration stories

If you are already on MongoDB or Oracle, moving to PostgreSQL is not a vague someday project—it hits budget, performance, and operations immediately. This post stays between the two lazy extremes (“trivial” vs “impossible”): what triggered real MongoDB→Postgres and Oracle→Postgres moves, how long they took, what hurt more than expected, and what changed afterward. Figures such as Infisical’s reported database cost reduction after leaving MongoDB, or wide TCO improvement narratives away from Oracle, come from public write‑ups, case studies, and vendor materials—so you should separate license vs labor vs migration project costs and read them as directional, not universal guarantees. The article also stresses that schema redesign and query work often travel with the database change, so “Postgres magic” and “refactoring wins” should not be collapsed into one headline. It closes with a compact decision frame so you can judge whether migration is a sensible next step for your team—not a moral obligation.

Read

Why PostgreSQL? Part 3 — Startups: the sweet spot of speed and cost

Part 2 explained why large tech companies keep PostgreSQL in the stack; this installment shifts to early product teams where the decision is usually about shipping an MVP quickly, keeping costs predictable, and avoiding a painful rewrite a few quarters later. For web and AI products, PostgreSQL has become the practical default—but much of the perceived ease comes from managed platforms such as Supabase and Neon and from extensions like pgvector, not from pretending the engine alone removes all work. The article separates relational modeling and extensions from provisioning, pricing, and developer experience, and it reads ARR estimates, YC adoption figures, and vector benchmarks as different axes with different definitions. It also stresses workload assumptions behind vendor benchmarks and closes with the operational habits—schema discipline, migration safety, backups—that determine whether the same Postgres choice stays cheap in production.

Read

Explore this topic·Start with featured series

한국어

Follow new posts via RSS

Until the newsletter opens, RSS is the fastest way to get updates.

Open RSS Guide