AWS and Cloud Engineering
Practical guides to load balancing, scaling, deployment, and resilient AWS foundations.
TechEazy field notes
Clear, hands-on notes on cloud engineering, Java and Spring, GitHub collaboration, and the career foundations that make technical work credible.
Practical guides to load balancing, scaling, deployment, and resilient AWS foundations.
Build a sound Java foundation, then apply Spring Boot, dependency injection, MVC, and REST.
Learn repository, branch, pull-request, protection, and delivery workflows used by engineering teams.
Durable guidance for choosing core skills, presenting your work, and building credible career evidence.
Technology Career Foundations
A grounded transition plan for backend, QA, DevOps, and other technical professionals moving into applied AI work without starting their careers over.
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Compare two AI-enabled technical paths by the work they require: shipping close to users or designing the system shape, boundaries, and trade-offs.
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A four-to-six-month part-time shape for backend developers moving toward FDE work through AI fluency, codebase delivery, and inspectable evidence.
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A practical path for experienced software professionals who want to move into client-facing AI delivery without treating a degree as the whole credential.
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A decision framework for experienced technical professionals evaluating an AI/ML program: inspect the sequence, evidence, feedback loop, and claims.
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A practical look at the recurring work behind Cloud/AI Solutions Architect roles: discovery, system choices, risk, cost, and technical communication.
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Learn how EC2 Auto Scaling Groups use launch templates, capacity limits, health replacement, and scaling policies to manage fleets safely.
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Understand AWS Elastic Load Balancing, including ALB, NLB, GWLB, listeners, target groups, health checks, security, and common limits.
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Turn verifiable projects into clearer resume bullets, a reviewable GitHub portfolio, an honest professional profile, and a scoped freelance offer.
Read the guide →Java and Spring Engineering
Build a small Spring Boot task API and understand how MVC request mapping, a service layer, constructor injection, and JSON responses fit together.
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Create and run a first Spring Boot REST application with Java 17+, Spring Initializr, Maven, and one small JSON endpoint.
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Deploy a Spring Boot JAR to EC2, run it as a service, and route traffic through an AWS Application Load Balancer with safe health checks.
Read the guide →Git and GitHub Collaboration
Choose branch protection or rulesets, require reviews and checks, control bypasses, and verify safeguards on important GitHub branches.
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Create a GitHub repository, work on a topic branch, open a clear pull request, respond to review, and merge with confidence.
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Safely connect an existing Spring Boot project to GitHub, exclude secrets and build output, authenticate, and push the first commit.
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A beginner roadmap for programming, web APIs, Git, data, testing and deployment—built around one progressive, reviewable project.
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Understand Spring IoC, beans, component scanning, and constructor injection through a small Spring Boot example that is easy to test.
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Improve a Spring Boot REST API with request DTOs, Bean Validation, correct HTTP status codes, centralized errors, and focused MVC tests.
Read the guide →Git and GitHub Collaboration
Understand how Git repositories, commits and branches relate to GitHub remotes, default branches, and pull requests.
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Scale a Spring Boot service on AWS with a versioned launch template, EC2 Auto Scaling Group, ALB target group, health replacement, and target tracking.
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An archived announcement of TechEazy's former Java, Spring Boot, AWS training and internship offer; its old terms are not currently available.
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Learn the difference between Java classes and objects with a complete Car example covering fields, constructors, methods, and independent state.
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