Learning AWS as a Java developer: where I'm starting and why

For the past few years I've been building Spring Boot microservices in an enterprise setting. I know how to ship services, but I've rarely made decisions about where and how they run in the cloud. I want to change that. This is the first post in a series where I document how I'm learning AWS as a Java developer: where I'm starting, why, and what I plan to build along the way.

Where I'm coming from

My day-to-day work is Java, Spring Boot and microservices, with Kafka for messaging and Kubernetes for deployment. Most decisions about the platform underneath (networking, permissions, environments) were already made by someone else. I use them, but I couldn't confidently design them from scratch.

Why AWS, and why now

My goal is to grow into a solutions architect. An architect needs to understand not only how a service works, but also where it runs, how it's secured, how it scales and what it costs. AWS is the most widely used cloud platform, so it's the natural place to build that foundation. I'm also preparing for the AWS Certified Solutions Architect Associate (SAA-C03) exam, and this series is how I'll keep my learning structured and public.

Where I am right now

I'm not starting from zero. So far I've covered IAM and the AWS CLI, plus the fundamentals of EC2. That gave me a first feel for how permissions work and how to create and manage resources from the terminal instead of clicking through the console.

How I'm learning

I'm using a free-tier account with a billing alert set up from day one, so experiments don't turn into surprise costs. I build small things, break them, and write down what I learned.

The next four weeks

I'm following the Solutions Architect Associate path, so my plan is built around it:

Week 1: EC2 at Solutions Architect level. Going deeper than the basics, so I can choose the right instance setup for a given workload.
Week 2: EC2 instance storage. EBS, instance store and EFS, and when each one makes sense.
Week 3: High availability and scalability. Elastic Load Balancers and Auto Scaling Groups, the core of building something that survives failure.
Week 4: Databases and caching. RDS, Aurora and ElastiCache, and how managed databases differ from running one myself.

After that I'll move on to S3, VPC and serverless with Lambda, and later look at how the tools I already use map to AWS (Kubernetes to EKS, Kafka to MSK or SQS).