Introduction to Module 5

Cloud computing fundamentals, vendor-agnostic skills, hands-on deployment.

What this module is about

Module 5 covers cloud computing — the platform that modern data work runs on. Twenty years ago, companies bought physical servers and kept them in a room. Today they rent computing power, storage, and databases from cloud providers: Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). Almost every data tool you have met in this course — Snowflake, BigQuery, Redshift, Databricks — lives in the cloud.

This module teaches cloud fundamentals in a vendor-agnostic way: the concepts are the same on AWS, Azure, and GCP, only the service names differ. You will learn the core service models, cloud security basics, how data platforms are built in the cloud, how to read a cloud solution architecture, and which cloud jobs and certifications are worth your time. Then you will practice with hands-on deployment.

Why the cloud matters for your career

Open any data engineer job posting. You will see AWS, Azure, or GCP in the requirements. Data analyst and BI roles increasingly expect cloud familiarity too, because the warehouse they query is a cloud service. Cloud skills are no longer optional for data careers — they are the environment the job happens in.

The good news: you do not need to know everything. Entry-level roles expect you to understand core concepts, navigate one provider’s console, and know the main data services. A single entry-level certification — like AWS Cloud Practitioner or Azure Fundamentals (AZ-900) — is achievable in a few weeks of study and is a real signal on a resume, especially for career changers with no prior tech employer.

Key concepts you will learn

The service models: IaaS, PaaS, SaaS

IaaS (Infrastructure as a Service): you rent virtual machines and storage — like AWS EC2. You manage the software; they manage the hardware.
PaaS (Platform as a Service): you rent a managed service — like a managed PostgreSQL on Amazon RDS. They patch and back it up; you just use it.
SaaS (Software as a Service): you rent finished software — like Snowflake or Tableau Cloud.

Data work mostly lives in PaaS and SaaS. Knowing where a service sits tells you what you are responsible for.

Core building blocks

Every cloud has the same primitives with different names: virtual machines (EC2, Azure VMs, Compute Engine), object storage (S3, Blob Storage, Cloud Storage), networking, and identity and access management (IAM). Object storage matters most for data people — it is where data lakes live, holding files like CSV and Parquet at almost no cost.

Cloud security basics

The shared responsibility model: the provider secures the cloud, you secure what you put in it. You will learn IAM roles and permissions, why you never hardcode credentials, and why a public S3 bucket is the classic data breach. Security questions appear in interviews because security mistakes in the cloud are expensive and public.

Data in the cloud

How the pieces from earlier modules map onto the cloud: cloud data warehouses (Snowflake, BigQuery, Redshift), data lakes on object storage, and managed pipeline services. This is where the modern data stack lives.

Solution architecture

How to read and draw the standard architecture diagram: sources feeding a lake or warehouse, transformation on top, BI at the end. Drawing this diagram on a whiteboard is a common interview exercise for data engineering roles.

Common beginner mistakes

Trying to learn all three clouds at once. Pick one — AWS has the most jobs overall, Azure dominates large enterprises. The concepts transfer; learn one deeply.
Fearing the bill. Beginners avoid hands-on practice because of horror stories. Use the free tier, set a billing alert on day one, and delete resources when done. That covers you.
Collecting certificates without practice. A certification plus one deployed project beats three certifications and no project.
Skipping IAM. Permissions feel boring, but half of real cloud debugging is access issues. Learn IAM early.
Memorizing service names instead of concepts. There are hundreds of services. Data roles use about a dozen. Learn what compute, storage, and managed database mean; the names follow.

How to work through this module

Create a free-tier account with one provider and set a billing alert immediately.
Follow the hands-on lessons — deploying something yourself is worth ten videos watched.
After the fundamentals lessons, sketch the standard data architecture diagram from memory.
If a certification fits your plan, use the cloud jobs and certifications lesson to pick one and set a date.

Next steps

See all lessons in Module 5: Cloud Computing.
Deploy something real with a pet project — a cloud-hosted project is strong evidence of skill.
See how cloud skills fit your target role in Getting your first data job.