An UiPath and Python RPA based automates data extraction, Excel creation, and billing portal order submission with error handling.
Built on AWS, this scalable platform integrates mobile and contact center systems for real-time health data and patient interaction.
A Python-Django based platform on AWS enables efficient video uploads, processing, and retrieval with automated, asynchronous workflows.
A mobile app for seamless control of indoor and outdoor LED lighting with themes, scheduling, and device grouping.
Advanced tracking for Shopify, Webflow, and WordPress using browser and server-side methods with platform-wide integrations.
A dual-role platform to instantly find or list parking spots with real-time availability, price comparison, and easy booking.
Integrates with Time & Attendance and Access Control systems to send real-time updates, alerts, and summaries via WhatsApp.
An UiPath and Python RPA based automates data extraction, Excel creation, and billing portal order submission with error handling.
Built on AWS, this scalable platform integrates mobile and contact center systems for real-time health data and patient interaction.
A Python-Django based platform on AWS enables efficient video uploads, processing, and retrieval with automated, asynchronous workflows.
A mobile app for seamless control of indoor and outdoor LED lighting with themes, scheduling, and device grouping.
Advanced tracking for Shopify, Webflow, and WordPress using browser and server-side methods with platform-wide integrations.
A dual-role platform to instantly find or list parking spots with real-time availability, price comparison, and easy booking.
Integrates with Time & Attendance and Access Control systems to send real-time updates, alerts, and summaries via WhatsApp.
An UiPath and Python RPA based automates data extraction, Excel creation, and billing portal order submission with error handling.
Built on AWS, this scalable platform integrates mobile and contact center systems for real-time health data and patient interaction.
A Python-Django based platform on AWS enables efficient video uploads, processing, and retrieval with automated, asynchronous workflows.
A mobile app for seamless control of indoor and outdoor LED lighting with themes, scheduling, and device grouping.
Advanced tracking for Shopify, Webflow, and WordPress using browser and server-side methods with platform-wide integrations.
A dual-role platform to instantly find or list parking spots with real-time availability, price comparison, and easy booking.
Integrates with Time & Attendance and Access Control systems to send real-time updates, alerts, and summaries via WhatsApp.
Python-Powered Video Processing Platform on AWS for Scalable Media Workflows
This Django-based Video Processing Platform offers a robust, cloud-native solution for managing video workflows from upload to processing and final result delivery. Built on scalable AWS services, it enables seamless integration between user interactions, background processing, and storage infrastructure. The platform ensures high availability, secure data handling, and real-time job status updates for an efficient and user-friendly experience.
By leveraging containerized processing with ECS Fargate, event-driven triggers through AWS Lambda, and decoupled workflows via SQS queues, the solution delivers scalable, asynchronous processing without overloading the core Django backend. It is ideal for platforms needing reliable, high-throughput video handling capabilities in a secure and maintainable architecture.
Built on Django, the web backend manages user access, video jobs, and system configurations.
It provides an intuitive interface for initiating and monitoring video processing tasks.
Ensures control, transparency, and efficient user interaction across the platform.
Video jobs are handled asynchronously using SQS for queuing and ECS Fargate for execution.
Each task runs in a containerized environment, allowing flexible and efficient scaling.
AWS Lambda supports event-driven orchestration, triggering workflows automatically.
All services communicate through a central API Gateway, enabling secure and unified routing.
Container images are stored safely in Amazon ECR, ensuring trusted deployment at scale.
Backend logic connects seamlessly through clean API endpoints and service integrations.
Amazon S3 handles scalable object storage for video files and related assets.
Structured metadata and relational data are stored securely in Amazon RDS.
The architecture supports high availability, durability, and data integrity.
Amazon CloudWatch tracks system health, performance, and operational events in real time.
The platform maintains a clean separation of responsibilities across services and components.
This design ensures maintainability, scalability, and simplified troubleshooting.
Building a scalable and efficient video processing system presents architectural and performance challenges. Ensuring seamless task coordination and system reliability was a core focus during development.
Decoupled Workflow Design: Avoiding direct dependency between upload and processing workflows required a robust queuing and worker model.
We implemented a modular, event-driven architecture that separates concerns, scales independently, and leverages AWS-managed services for performance, reliability, and low maintenance overhead.
Asynchronous Job Pipeline
Containerized Processing with ECS Fargate
Real-Time Status Updates
Durable Storage in Amazon S3
Monitoring and Alerts via CloudWatch
End-to-End Cloud Video Processing Infrastructure
Django-based backend development providing scalable, secure, and efficient server-side application support.
AWS S3 setup enabling reliable, scalable file storage and seamless file management for your applications.
Asynchronous task handling powered by Amazon SQS to ensure smooth, decoupled workflow processing.
Containerized video processing using ECS Fargate and secure image storage with Amazon ECR for scalable execution.
Real-time job status management with Amazon RDS and AWS Lambda for accurate task monitoring and orchestration.
Full system logging, performance monitoring, and alerting provided through Amazon CloudWatch for proactive maintenance.
By handling video workloads asynchronously and in parallel, the system significantly reduced processing wait times. This allowed users to upload and receive results much faster, improving turnaround and overall platform responsiveness.
The use of queued workflows and containerized jobs helped isolate tasks, reduce failure points, and enable automatic retries. This architecture ensured higher reliability and minimized disruption during high-load or error-prone operations.
With no servers to manage, the system leverages AWS Lambda and ECS Fargate to run compute tasks only when required. This event-driven model optimized infrastructure costs while maintaining scalability and performance.
The Django interface delivers real-time updates on video job statuses, including pending, processing, and complete. This transparency ensures users stay informed throughout the process, enhancing trust and user satisfaction.
The architecture is designed to scale effortlessly as user demand or video volume grows. Minimal changes to the core system are needed, ensuring long-term sustainability and reduced maintenance effort.
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