Aditya Deokar
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© Aditya Deokar // Portfolio

Elevated digital experiences

[ 2024 / Full-Stack Architect & Lead Developer / 8 months ]

Artistry Cart

A Multi-Vendor E-commerce SaaS Platform, Architected for Scale.

Live Execution
Access Root
Vol. 2024 / 8 months

Classification

SaaS & E-Commerce

Publication

Project Overview

Overview &
Analysis

A sophisticated multi-vendor e-commerce platform built with a microservices architecture, designed to empower independent artists and small creators with powerful, affordable e-commerce tools.

"The goal was to build a resilient, scalable ecosystem that empowers creators to build their own brand and business, free from platform constraints."

Architectural Vision
Fig. 01

Challenge

Independent artists lacked access to powerful, affordable e-commerce tools, forcing them onto crowded, high-fee marketplaces.

Fig. 02

Solution

A scalable, microservices-based SaaS platform providing creators with their own branded storefronts and powerful tools.

Fig. 03

Impact

Onboarded 1,200+ creators, achieved 99.95% uptime, and reduced vendor onboarding time from 30+ minutes to under 5.

Fig. 04

Innovation

Utilized an event-driven architecture with Kafka and a TensorFlow-based AI engine to boost recommendations by 15%.

Performance Metrics

Empirical Outcomes &
System Efficacy

1,200+
Active Vendors
99.95%
Platform Uptime
120ms
API Response (P95)
+15%
Recommendation CTR
Vol. II / Capabilities

Engineered
Features

Advanced system implementations architected for absolute computational robustness.

01

Scalable Microservices Architecture

The platform is built on a distributed system of microservices, communicating asynchronously via Kafka. This design decouples services, ensures fault tolerance, and allows for independent scaling of components.

99.95%Uptime
120msP95 Response
ResilientSystem
Index TermsMicroservicesKafkaFault Tolerance
02

Zero-Downtime DevOps Pipeline

A fully automated CI/CD pipeline built with GitHub Actions and Kubernetes ensures continuous availability. The NX Monorepo architecture reduced CI times from 20 minutes to just 6 by only building and testing affected services.

70%Faster Builds
ZeroDowntime
AutomatedDeployments
Index TermsCI/CDKubernetesNX Monorepo
03

AI-Powered Recommendation Engine

Utilizes a TensorFlow model to provide personalized product suggestions, increasing user engagement and conversion rates. The engine analyzes user behavior to surface relevant art and products.

+15%CTR Increase
AI-DrivenDiscovery
SmartSuggestions
Index TermsAI/MLTensorFlowPersonalization

Project Demo

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Vol. III / Methodology

Development
Process

A journey from concept to creation, navigating challenges and delivering excellence.

001
The Why

The Spark

Identified that independent artists lacked powerful, affordable e-commerce tools, forcing them onto crowded marketplaces. The goal was to build a scalable SaaS platform to empower 1,000+ creators.

Phase KeywordsProblem DefinitionHypothesisUser Research
002
The How

The Architecture

Designed a distributed, event-driven system using a microservices architecture. Key choices included Kafka for asynchronous communication to ensure resilience and Redis for caching to boost performance.

Phase KeywordsSystem DesignArchitectural PatternsTechnology Selection
003
The What

The Build

Developed services in a monorepo, focusing on clean separation of concerns. Implemented event publishing and consumption for core business logic like order processing to create a non-blocking, resilient system.

Phase KeywordsDevelopmentEvent-Driven LogicCode Implementation
004
The DevOps Flow

The Assembly Line

Established a fully automated CI/CD pipeline using GitHub Actions, Docker, and Kubernetes. Implemented rolling updates for zero-downtime deployments and optimized build times with NX.

Phase KeywordsCI/CDContainerizationDeployment Strategy
Project Duration

8 months of focused development

4Phases
15+Technologies
70%CI Time Reduction
99.95%Uptime
[ Architecture ]

Engine
capabilities.

A rigorously curated array of industry-grade topologies mapping backend frameworks, frontend parameters, and deployment paradigms.

[ FRONTEND NODE ]

/ 01

Next.js

"For high-performance, server-rendered React applications."

/ 02

Tailwind CSS

"A utility-first framework for rapid, custom UI development."

/ 03

NX Monorepo

"Managed frontend and backend in one repo for efficient, scalable development."

[ BACKEND NODE ]

/ 01

Node.js

"Event-driven runtime for building scalable microservices."

/ 02

MongoDB

"NoSQL database used for flexible data storage across services."

/ 03

Kafka

"Asynchronous backbone for resilient, event-driven communication."

/ 04

Redis

"Caches popular products and sessions, improving response times by 60%."

[ AI NODE ]

/ 01

Python & Flask

"Served the recommendation model via a lightweight API."

/ 02

TensorFlow

"Powered the recommendation engine for personalized product suggestions."

[ DEVOPS NODE ]

/ 01

Docker

"Containerized each microservice for consistent, isolated deployments."

/ 02

Kubernetes

"Managed and scaled containerized applications with zero-downtime deployments."

/ 03

GitHub Actions

"Automated the build, test, and deployment pipeline."

15+Total Nodes
95%TS Config
15+Total Nodes
95%TS Config
15+Total Nodes
95%TS Config
15+Total Nodes
95%TS Config
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