One of the most interesting projects I’ve worked on recently involved building the frontend for a real-time, data-intensive web application. While I can’t reveal the client or project name, I can definitely share the challenges, solutions, and learning experiences that came out of it.
The application was built using Angular on the frontend and Python on the backend, and my responsibility was to design and implement the entire UI/UX layer — especially the real-time dashboard.
The Core Idea: Real-Time News Feed:
The main feature of the application was a real-time news dashboard. Users could subscribe to different categories or “areas,” mark some of them as favorites, and view incoming news in an organized, constantly updating interface.
The unique part?
The dashboard wasn’t loading news on refresh or a fixed interval — it was streaming live updates.
Every second, each category was receiving around 10 to 20 new news items, and the UI had to:
- Display them instantly
- Keep everything smooth
- Avoid freezes or memory leaks
- Maintain read/unread status
- Highlight favorites
- Handle thousands of items efficiently
This turned into a great exercise in performance optimization and real-time UI handling.
Using SignalR for Real-Time Updates:
To stream the incoming news feed, we integrated SignalR, which handled live, bi-directional communication with the backend.
On the frontend, I used:
- TypeScript for handling event streams
- jQuery for DOM manipulation in a few specific cases
- Angular services and components to manage data and UI rendering
Each SignalR event carried multiple items, and I had to decide how to push them into the UI without blocking rendering.
Some of the things I implemented:
- Efficient array management to prevent memory buildup
- Chunked rendering for smoother UI
- Smart scroll handling so users don't lose position when new items arrive
- Event throttling to avoid too many DOM updates at once
- Categorized pipelines so each subscribed area gets its own stream
This part of the project taught me a lot about handling high-frequency live data on the frontend.
Managing Users, Subscriptions & Preferences:
The dashboard wasn’t just a passive viewer.
Every user had their own preferences, such as:
- Subscribed categories
- Favorite areas
- Read/unread behavior
- Notification settings
- Profile settings
All of these were coming from the backend through REST APIs.
I designed the pages for:
- User profile updates
- Subscription management
- Favorite category settings
- Dashboard customization
The UI had to be simple enough for everyday users, yet fast enough to handle a stream of continuous data.
Heavy API Integration on the Frontend:
Along with the real-time SignalR channel, we integrated a full API layer to fetch:
- User data
- Subscription lists
- Historical news
- Read/unread tracking
- Category metadata
I handled:
- Request/response formatting
- Error handling
- Loading skeletons
- State management
- Refresh logic without losing real-time updates
Combining API-based data with streamed data was one of the biggest architectural challenges, but it worked beautifully once everything aligned.
What I Learned:
This project taught me more than just coding. Working on it strengthened my understanding of:
- Building highly reactive interfaces
- Working with live data at scale
- Managing complex UI state
- Optimizing Angular performance
- Combining Angular + TS + jQuery intelligently
- Thinking from the user’s perspective
- Handling edge cases in real-time applications
Most importantly, it reminded me how much I enjoy building products that feel alive — where users don’t have to refresh, reload, or wait. Everything just happens.
Conclusion:
Real-time applications come with their own challenges, but they’re incredibly rewarding to build.
Seeing live data flow into the dashboard every second, updating seamlessly without a single refresh, is a feeling every frontend developer loves.
This project helped me grow as a developer and pushed me to improve my approach to performance, architecture, and user experience.
I’m looking forward to working on more real-time systems in the future and sharing what I learn along the way.