📘 01-04: Web Apps, Warehouses, Big Data & Cloud¶

This note is scene-setting. Each idea here gets a full module later in the course — the goal right now is just to recognize the terms and see how they connect to the tables you've been reading about.

📌 Web Database Applications¶

A web database application is a web app whose front end (a browser or mobile screen) never talks to the database directly. Instead:

The user does something in the browser (submits a form, clicks a link).
The web app calls an API (Application Programming Interface) — often over HTTP, using JSON as the data format.
Server-side code (Python, JavaScript/Node, PHP, Java, ...) turns that request into SQL.
The SQL runs against the DBMS, and results flow back up the same chain.

Browser  →  Web/API server  →  SQL  →  DBMS  →  Database
   ↑                                              │
   └──────────────── JSON response  ◄─────────────┘

This is why "the website is down" is so often actually "the database connection is down" — the browser is just a window onto data sitting in tables, several layers removed.

📌 OLTP vs. OLAP — A Teaser¶

Databases get used for two very different jobs, and the same table design isn't always good at both:

	OLTP (Online Transaction Processing)	OLAP (Online Analytical Processing)
Job	Record what's happening right now — an order, a payment, a login	Analyze history — trends, totals, patterns over time
Typical query	"Insert this order" / "What's the status of order #4471?"	"What were total sales by region for the last 8 quarters?"
Optimized for	Fast, small, frequent read/write transactions	Large, complex, read-only aggregations
Example system	The database behind a checkout page	A company's data warehouse / BI dashboard

A single production database is usually an OLTP system. Running heavy analytical queries directly against it can slow down the very transactions it exists to process — which is one reason companies copy data out into a separate data warehouse built for OLAP instead.

Note

This is only a preview. Module 07 covers data warehouses, star schemas, and business intelligence (BI) tooling in depth.

📌 Big Data and NoSQL — A Teaser¶

Big Data refers to datasets so large, fast-moving, or unstructured that traditional relational tables start to strain — think billions of sensor readings, social media posts, or clickstream events per day.

NoSQL ("Not Only SQL") databases are a family of non-relational engines built for this scale and shape of data: document stores (MongoDB), key-value stores (Redis), wide-column stores (Cassandra), and graph databases (Neo4j, ArangoDB) are all common examples.
They typically trade some of the strict consistency and rigid schema of a relational database for horizontal scalability and flexible, schema-light documents.

This course is a relational database course — but it's worth knowing NoSQL exists so you recognize when a project's shape (huge scale, loosely structured data, need for horizontal scale-out) might call for it instead of, or alongside, a relational engine.

📌 Cloud Computing — A Teaser¶

Cloud computing means running your database (and everything else) on someone else's hardware, rented over the internet, instead of buying and maintaining your own servers.

Provider	Managed relational database offering
Amazon Web Services (AWS)	RDS (PostgreSQL, MySQL, SQL Server, ...), Aurora
Microsoft Azure	Azure SQL Database, Azure Database for PostgreSQL
Google Cloud Platform (GCP)	Cloud SQL, AlloyDB

A managed database service handles backups, patching, replication, and scaling for you — you connect to it exactly like any other PostgreSQL/MySQL/SQL Server instance, using the same SQL you're learning in this course, but you never touch the physical machine it runs on.

Tip

Everything in this course — every CREATE TABLE, every SELECT — runs identically whether the database sits on your laptop or inside AWS RDS. The SQL doesn't change; only who manages the server underneath it does.

All Notes | Module 01 Exercise | Next: Relations & Terminology