The Best AWS, Azure, and GCP Courses For Aspiring Data Cloud Engineers

If you search for a cloud data engineering course, you might be overwhelmed by the sheer number of resources and unsure which, or how many, of the big 3 providers to learn. The answer the community keeps landing on is to start with one and only pick up a second cloud once the first choice feels boring. If you now ask yourself, which one to pick, here's a decision framework for you:

• Learn AWS first if you want the widest set of job postings
• Learn GCP first if the work you want is BigQuery and Dataflow-heavy
• Learn Azure first if your employer already runs SQL Server and Power BI.

This list is for people moving into a data cloud engineering role: analysts who want to own pipelines, software engineers moving toward data platforms, and working data engineers who run on-premise Spark and now need Glue, Synapse, or Dataflow. I chose these resources based on whether the syllabus maps to a real certification blueprint (CLF-C02, AZ-900, DP-203, Google's Professional Data Engineer), covers data services rather than general cloud admin only, and is recommended by practitioners.

I have assumed you know some Python and SQL. If you don't, that gap matters more than which cloud you pick. Before you pick a cloud, close that gap with DataCamp's Associate Data Engineer in SQL career track, which builds the SQL and ETL/ELT foundations every managed-Spark service assumes.

TL;DR

1. DataCamp: Understanding Cloud Computing Course

The Understanding Cloud Computing course is the right starting point if you have never had an AWS, Azure, or GCP account and want the shared vocabulary before you start clicking around a console. The course is conceptual rather than hands-on, which is exactly what you want at hour zero.

It covers the service models (IaaS, PaaS, SaaS), the deployment models (public, private, hybrid), how the major providers differ, and where security and governance responsibilities sit between you and the vendor. Because it is provider-neutral, you are able to read an AWS Glue tutorial and an Azure Data Factory tutorial and see that they are solving the same problem with different nouns. #

The course runs in the browser, so there is no local setup and no cloud bill while you learn. A personal plan costs roughly $25 to $33 per month, billed annually, for unlimited access to all the DataCamp courses listed in this list.

• Level: Beginner, no cloud account required
• Format: Interactive course, no coding prerequisites
• Best for: Analysts and career switchers who need cloud concepts before cloud consoles

2. DataCamp: AWS Cloud Practitioner (CLF-C02) Skill Track

If the r/dataengineering consensus points you to Amazon's cloud, the AWS Cloud Practitioner (CLF-C02) skill track is the cheapest way to get the service landscape into your head in one structured pass. It is built around the CLF-C02 exam blueprint rather than a random tour of the console.

You'll cover cloud concepts, core AWS services (including S3, EC2, and IAM), security and the shared responsibility model, and the billing and pricing side that most beginners ignore until they leave an EMR cluster running over a weekend. For a data engineer, the billing chapters earn their place: cost control is part of the job description, not an afterthought.

CLF-C02 is a foundational certification, so it will not teach you how to write a Glue job or model a Redshift schema. Treat it as the map, then go to the AWS Skill Builder Data Engineer path at entry 6 for the actual pipeline work.

• Level: Beginner, aligned to the CLF-C02 exam blueprint
• Format: Skill track, multiple interactive courses
• Best for: Learners committing to AWS first because of job-market breadth

3. DataCamp: Microsoft Azure Fundamentals (AZ-900) Skill Track

Start here instead of the AWS track if your employer already runs SQL Server, Power BI, and Active Directory, which is the case for a large share of enterprise data teams. AZ-900 is the entry point Microsoft designed for exactly this situation.

The Microsoft Azure Fundamentals (AZ-900) track walks through Azure core concepts, the main service families, management and governance, and pricing and support options. You will meet Azure Blob Storage and Data Lake Storage here as service names, which is enough context to make the DP-203 material readable rather than baffling.

One practical note on cost: the learning is one bill, and the certificate is another. The AZ-900 exam itself runs about $99 to $120, depending on your region, and you do not need to sit for it to get value from the track. I would only pay for the exam if a job posting or an internal promotion process specifically asks for it.

• Level: Beginner, minimal prerequisites
• Format: Skill track mapped to AZ-900 domains
• Best for: Data professionals inside Microsoft-heavy organizations

4. John Savill's Technical Training (YouTube)

Savill's free YouTube playlists are the reason you don't necessarily have to pay for Azure foundations training. His AZ-900 and AZ-104 study series are widely cited as a free but good starting point.

The AZ-104 material is administrator-focused, and that is the point for a data engineer: identity, networking, storage accounts, and governance are what you will be arguing with when your Data Factory pipeline cannot reach a private endpoint. He also runs live study sessions and whiteboard explanations that hold up better than many paid videos.

What you will not get here is interactive learning or pipeline design. There is no Synapse dimensional modeling, no incremental load patterns, and no dbt. Use the playlists to shore up the infrastructure layer, then spend your study time on the DP-203 path.

• Level: Beginner to intermediate
• Format: Free YouTube playlists and live study sessions
• Best for: Azure learners on a zero budget who need infrastructure fundamentals

5. DataCamp: Introduction to GCP Course

Pick DataCamp's Introduction to GCP course if your target work is analytics-heavy, because GCP gets consistent praise in r/dataengineering threads for its data tooling and for how little ceremony BigQuery demands compared to setting up a Redshift cluster.

The course introduces the GCP service landscape, storage and compute options, and how BigQuery fits into an analytics stack. The reason I put it above the vendor material is sequencing: Google Cloud Skills Boost labs assume you already know what a project, a service account, and a region are, and this course gives you that in a couple of sittings.

The honest limitation is depth. You will not come out of an introductory course able to design a streaming pipeline with Pub/Sub and Dataflow, and you should not expect to. That is what the next entry is for.

• Level: Beginner
• Format: Interactive course, browser-based
• Best for: Analysts and engineers heading toward BigQuery-centric data work

6. DataCamp: Google Cloud Data Engineer Skill Track

The Google Cloud Data Engineer skill track on DataCamp is the most direct route on the list from "I know some SQL" to "I can build a pipeline on GCP," and it is the track I would point most career switchers toward. It stays on data services rather than wandering into general cloud administration.

The focus is the data engineer's side of GCP: storage, BigQuery, and moving data through pipelines, in the same territory Google's Professional Data Engineer certification covers with BigQuery, Dataflow, Pub/Sub, Cloud Composer, and Dataproc. Working through it in the browser means you are not juggling billing alerts and IAM errors while also learning windowed aggregations for the first time.

One thing you should know: It assumes Python and SQL, and if you are shaky on either, you will spend your time debugging syntax rather than learning pipeline design. DataCamp also offers the Associate Data Engineer in SQL career track to close that gap before going deep into GCP.

• Level: Intermediate, assumes Python and SQL
• Format: Skill track, multiple interactive courses and projects
• Best for: Aspiring data engineers who have chosen GCP and want practice, not slideware

7. Adrian Cantrill's AWS Certified Solutions Architect Associate (SAA-C03)

Cantrill's SAA-C03 course keeps turning up as the community's pick for best AWS foundations, and it earns that reputation through depth on the plumbing rather than breadth across shiny services. 

For a data cloud engineer, the value is concentrated in the sections that other data courses skim: IAM roles and policies, VPC networking and endpoints, storage classes and lifecycle rules, and encryption with KMS.

It is a paid course sold directly from Cantrill's own site, with pricing set there and varying with promotions, and it is a long commitment measured in weeks rather than evenings. It is also not a data engineering course, so treat it as the layer underneath entry 11 rather than a replacement for it.

• Level: Intermediate, assumes a basic IT background
• Format: Paid video course with labs and architecture diagrams
• Best for: Engineers who want to understand why an AWS pipeline breaks, not just how to build one

8. Google Cloud Skills Boost: Data Engineer Learning Path

Google's own training platform is the best place to get hands-on GCP practice without provisioning anything yourself, because the labs in the Data Engineer Learning Path hand you a temporary project with the APIs already switched on. Several of the courses and labs are free.

The data engineer path covers BigQuery for warehousing and SQL analytics, Dataflow for batch and streaming transformation, Pub/Sub for event ingestion, Cloud Composer for Airflow-based orchestration, and Dataproc for managed Spark. That service list is essentially Google's Professional Data Engineer exam blueprint, so the path doubles as certification prep.

Costs vary by route. Free labs and quests cover a lot of ground, while partner-hosted versions of the same tracks typically sit in the $39 to $79 per month subscription range, so check whether the free path already gets you what you need before paying twice for the same syllabus.

• Level: Intermediate
• Format: Vendor courses plus timed hands-on labs, free and paid tiers
• Best for: GCP learners who want console practice without a personal billing account

9. Grow Data Skills: Data Engineering Tracks for AWS, Azure, and GCP

Grow Data Skills runs separate self-paced tracks per cloud, and its selling point is portfolio output rather than lecture hours. For someone with no cloud work history, the projects matter more than the certificate.

Each track starts with fundamentals like data modeling, warehousing, and the batch versus stream distinction, then moves into cloud-specific ingestion, storage, transformation, and orchestration modules. The project work is the differentiator: end-to-end pipelines and lakehouse architectures you can put in a repository and talk through in an interview.

It is a paid track with pricing shown at checkout and varying by region and promotion, and it assumes basic Python and SQL, plus some cloud familiarity. If you have not finished a beginner track yet, do that first, or you will spend the projects copying commands you do not understand.

• Level: Intermediate
• Format: Self-paced video plus guided projects, paid
• Best for: Career switchers who need three or four portfolio pipelines, not another certificate

10. Smart Data Learning: Data Engineering With Azure, AWS & GCP

This is a 14-week live virtual program that covers all three clouds in one syllabus, and it is the option to consider if self-paced video has already failed you twice. Deadlines and a cohort work for some people, and pretending otherwise is bad advice.

The structure runs core data engineering concepts first, then cloud-specific implementations: S3, Glue, Redshift, and EMR on AWS; Data Lake, Data Factory, and Synapse on Azure; BigQuery, Dataflow, and Pub/Sub on GCP. Assignments and labs run alongside the live sessions, so there is graded work rather than passive watching.

Prior Python and SQL experience is recommended, and honestly required. The pricing is a fixed program fee listed on the course page and varies by region, which is less transparent than I would like; ask for the number in writing before you commit to 14 weeks.

• Level: Intermediate; prior programming experience, Python, and SQL recommended
• Format: Live virtual cohort, 14 weeks, assignments and labs
• Best for: Learners who need scheduled instruction and want all three clouds in one pass

11. AWS Skill Builder: AWS Certified Data Engineer – Associate Learning Path

This learning path on AWS Skill Builder is where AWS learners should go after CLF-C02, because it is the only AWS resource on this list built specifically around data engineering services rather than architecture in general. It is Amazon's own training platform, so the content tracks the current exam guide.

The path centers on the services you will be paid to run: AWS Glue for ETL, Lake Formation for governed data lakes, Amazon Redshift for warehousing, EMR for Spark and Hadoop workloads, and Kinesis for streaming ingestion. Pricing is a mix, with a meaningful chunk of digital training free and the Skill Builder Individual subscription at roughly $29 per month or $299 per year for the labs and exam-prep material.

The difficulty jump from CLF-C02 is real. This material targets exam-level depth and assumes you can already navigate the console, read IAM policies, and reason about VPC placement, so do not start here as your first AWS exposure.

• Level: Advanced; recommended after CLF-C02 and some hands-on experience
• Format: Vendor learning path, free courses plus paid subscription tier
• Best for: Engineers targeting the AWS Certified Data Engineer – Associate exam

12. Microsoft Learn: Azure Data Engineering (DP-203) Path

The best free advanced Azure data engineering curriculum comes from Microsoft itself, and the DP-203 path is the one to work through once AZ-900 concepts have landed. All the Learn modules are free to read and work through.

Coverage is the Azure data stack proper: Azure Data Lake Storage, Synapse Analytics for warehousing and Spark pools, Data Factory for orchestration and copy activities, Stream Analytics for streaming queries, and Azure Databricks for notebook-based transformation. If your team is standardizing on Fabric-era Synapse workloads, this is the material that maps closest to your day job.

Two honest problems. Microsoft Learn is reading-heavy and can feel like documentation with a progress bar, so pair it with your own subscription and build something rather than clicking Next. And the DP-203 exam fee is around $165, the single most expensive item on this list per hour of learning.

• Level: Advanced; recommended after AZ-900 and some hands-on experience
• Format: Free self-paced modules, paid exam
• Best for: Azure data engineers who want vendor-accurate coverage at no course cost

13. r/dataengineering cloud-choice threads

The most useful free resource on this list is not a course at all: the r/dataengineering subreddit. Threads like "Which cloud provider is best to learn for someone just starting out in the Data Engineering field?" and "Data engineering in GCP, Azure, or AWS is best to upskill and switch" are where the actual decision gets made, and the practitioners answering are the people who sit on hiring panels.

The recurring positions are consistent enough to summarize: AWS is called the safest career investment because of market share, GCP gets recommended for data-heavy and ML workloads on the strength of BigQuery and Dataflow, and Azure wins when the employer is Microsoft-centric or the local market is. The other repeated piece of advice is to pick one cloud, get good at it, then add a second.

Read the threads for the regional detail that no course syllabus contains. Whether your city's job postings say Synapse or Snowflake is a question only local practitioners can answer, and it should override every other recommendation here.

• Level: Any
• Format: Free community discussion
• Best for: Deciding which cloud to commit to before spending money on training

What Skills a Data Cloud Engineer Needs

A data cloud engineer designs, builds, and runs data pipelines on AWS, Azure, or GCP, which in practice means ETL and ELT jobs feeding data lakes and warehouses, plus the security and cost controls around them. The job sits between software engineering and analytics, and the skill list reflects that.

• Programming and querying: Python and SQL, non-negotiable
• Object storage: Amazon S3, Azure Blob and Data Lake Storage, or Google Cloud Storage
• Warehouses and analytics engines: Redshift, BigQuery, or Synapse, and Snowflake when the shop is multi-cloud
• Orchestration: Airflow, plus the native schedulers (AWS Step Functions, Azure Data Factory pipelines, Cloud Composer)
• Streaming: Kinesis, Azure Event Hubs, or Pub/Sub, and Kafka, where teams run their own brokers
• Infrastructure as code: Terraform, CloudFormation, or Bicep
• Operations basics: Linux, networking, Git, and comfort in a shell

For the certification-level material further down this list, expect 6 to 12 months of hands-on cloud experience as the baseline. The DP-203 and AWS Data Engineer paths will not be kind to you before that.

Suggested Learning Path

Here is the order I would follow if I were starting out as an analyst today and wanted to be interviewing for cloud data engineering roles within a year.

Stage 1: Get the vocabulary and pick one cloud

Work through Understanding Cloud Computing, then read a couple of the r/dataengineering cloud-choice threads with your local job listings open beside them. Commit to one provider before you buy anything else.

Stage 2: Learn one provider's fundamentals properly

Take the AWS Cloud Practitioner (CLF-C02) track, the Microsoft Azure Fundamentals (AZ-900) track, or Introduction to GCP plus the first few courses of the Google Cloud Data Engineer track, depending on stage 1. If you chose Azure, have no budget at all, and don't need interactive content, John Savill's AZ-900 and AZ-104 playlists cover the same ground for free.

Stage 3: Go deep on that cloud's data services

This is where you become employable.

• GCP learners should finish the Google Cloud Data Engineer track and then run the free BigQuery, Dataflow, and Pub/Sub labs on Google Cloud Skills Boost.
• AWS learners move to the Skill Builder Data Engineer path for Glue, Lake Formation, Redshift, and Kinesis.
• Azure learners move to the free Microsoft Learn DP-203 path for Data Lake Storage, Synapse, and Data Factory.

Stage 4: Build projects and shore up the infrastructure layer

Ship two or three end-to-end pipelines you can explain, either through the Grow Data Skills project modules or on your own with Terraform and Airflow. In parallel, Cantrill's SAA-C03 course fills the IAM, VPC, and encryption gaps left open by data-only courses. If deadlines are your real problem rather than content, swap this stage for the 14-week Smart Data Learning cohort.

Final Thoughts

For most people reading this, the sequence is Understanding Cloud Computing, followed by one provider fundamentals track, and then the data services path for that provider. Two weeks of concept work saves you a month of confusion later, and picking one cloud beats sampling three.

One thing to know: Exam fees move, and the $99 to $120 for AZ-900, $165 for DP-203, and $299 per year for AWS Skill Builder are the figures at the time of writing, not guarantees.

Another important caveat is the one nobody selling a course wants to say out loud. None of these resources will get you hired on their own; the thing that gets you interviews is two or three pipelines you built, broke, and can explain, ideally with Terraform in the repository. Certificates open the screening call; projects win it.