Full-Stack[SYS_ID: MAKAANZA]

Scalable online marketplace platform with user listings, property search filters, and messaging workflows.

01.System Interface & Screenshots

https://makaanza.aqib.dev
Makaanza Interface Screenshot
[SCREENSHOT_01_ACTIVE]

02. Interactive Pipeline Simulation

Interactive System Architecture Simulator

Simulate processes and see live data flows, database locks, and logs in Aqib's backend pipelines.

System Node Architecture:
React App
Django API
PostgreSQL
AWS S3
AWS Lambda
Visual Feedback Window:
MAKAANZA ENGINE MONITORSTAGE: IDLE
Click "Start Simulation" to launch query & upload sandbox.
Pipeline Stage Info
Press "Start Simulation" to review detailed process annotations.
Pipeline Console Output:
Console output idle. Launch simulation to trace diagnostic outputs.

03. The Problem

Real estate search platforms suffer from slow filter query responses and lacks clean messaging interfaces between listing owners and buyers.

04. The Solution

Refined backend search APIs, designed optimized PostgreSQL database indexes, and built the frontend real estate listing feeds.

05. Key Features

  • >Multi-criteria advanced filters (price, location, features)
  • >User property listing upload with image hosting
  • >In-app buyer-seller chat system
  • >AWS S3 image storage with compression pipelines
  • >Geo-location property mapping

Backend & API Architecture

-PostgreSQL query optimization and full-text search indexes
-Scalable REST API endpoints with Django REST Framework
-AWS S3 secure file upload via pre-signed URLs

08. Engineering Challenges

Slow property search queries over thousands of listings

Implemented database indexes on key filter fields and cached common searches with Redis.

Hosting and serving uncompressed property images

Built a serverless resizing pipeline that scales images dynamically.

[SYSTEM_SPECIFICATIONS]
ROLE:
Software Engineer — Backend & Database
CATEGORY:
Full-Stack
TECHNOLOGY STACK:
DjangoReactPostgreSQLREST APIsTailwind CSSAWS S3
BUSINESS IMPACT:
Improved page response times by 35% and increased search-to-contact user actions by 18%.