Event-Driven Serverless Platform

Project Overview

This project treats serverless as an asynchronous operating pattern: producers, queues, function execution, retry behavior, dead-letter handling, audit trail, validation and cost control are documented as one delivery flow.

Engineering Problem

The project is designed around real platform constraints: repeatability, change control, security boundaries, failure handling and reviewable evidence.

• Process asynchronous work reliably without coupling producers directly to workers.
• Make retries, failure paths and audit evidence visible instead of treating them as afterthoughts.
• Design serverless flow that can be validated without uncontrolled runtime cost.

Architecture

The architecture is documented as a set of reviewable engineering decisions instead of a simple tool list.

• Event producers hand work to a queue so processing can absorb bursts and failures.
• Function execution is documented with retry behavior and dead-letter paths.
• Audit output and validation notes make the event lifecycle reviewable.
• Cost boundaries are part of the design because serverless systems can fail financially as well as technically.

Implementation Evidence

Implementation evidence follows the event lifecycle from intake through buffering, processing, failure handling and audited outcome.

• Repository content describes queue, function, retry, audit and failure-handling responsibilities.
• Validation artifacts show how the event path is checked from input through outcome.
• The implementation is framed as a reusable workflow pattern, not a single function example.
• Cost-control notes explain when live execution is needed and when evidence is enough.

Validation Evidence

Validation is captured in the repository so the project can be reviewed without relying on vague claims. Continuous checks cover Terraform, handler syntax, contract markers and unit tests for normal and empty event batches.

• Checks cover structure, documentation consistency and evidence traceability.
• Failure-path behavior is described so reviewers can see how the system handles non-happy paths.
• The source repository supports direct technical inspection.

Operating Tradeoffs

The work keeps cloud-spend exposure controlled while preserving credible engineering depth.

• The project documents live behavior without requiring constant event processing spend.
• Retry and dead-letter choices make reliability tradeoffs explicit.
• Validation favors repeatability and traceability over fragile manual proof.

What I Built

• Asynchronous event-processing pattern with queue-based decoupling.
• Function handler model with retry and dead-letter responsibilities.
• Audit output path for traceability from event intake through processing result.
• Validation notes covering normal processing, failure handling and cost boundaries.

Reliability Controls

• Queues absorb bursts and protect processors from direct producer coupling.
• Retries and dead-letter handling prevent failed work from being silently lost.
• Audit records support troubleshooting, accountability and operational review.
• Infrastructure and evidence remain reproducible without requiring permanent runtime spend.

Engineering Controls

Failure Analysis

The design considers duplicate delivery, processing timeout, poison messages, downstream unavailability and replay safety. Those conditions are treated as first-class workflow states rather than exceptional footnotes.

Completed Result

The completed project demonstrates a structured event-driven serverless platform with asynchronous processing, failure isolation, audit visibility and cost-aware validation. It presents the full operating lifecycle rather than only a basic function example.

Source Code

The repository contains the implementation, documentation and supporting evidence.

Open GitHub Repository

Engineering Value

This project shows event-driven ownership: lifecycle design, retry behavior, failure isolation, auditability, validation evidence and cost-aware execution.