#real-time

Build an interactive real-time analytics dashboard. Tech stack: 1. Use Plotly Dash for the web framework. 2. Implement WebSocket connections for live data streaming. 3. Create responsive charts (time series, heatmaps, scatter plots). 4. Add filtering and date range selectors. 5. Implement data aggregation with Pandas for performance. 6. Use Redis for caching frequently accessed metrics. 7. Add export functionality (CSV, PDF reports). 8. Implement role-based access control. Include dark mode toggle and mobile responsiveness.

Architect a real-time data pipeline using Apache Kafka. Components: 1. Producer sending clickstream events (JSON). 2. Kafka topic with 3 partitions for scalability. 3. Consumer group processing events in parallel. 4. Stream processing with Kafka Streams for aggregations. 5. Sink connector to write to Elasticsearch. Include error handling, exactly-once semantics, and monitoring with Kafka lag metrics.

Integrate Firebase for rapid development. Services: 1. Firestore for document database. 2. Collections and documents structure. 3. Real-time listeners with onSnapshot. 4. Compound queries with where clauses. 5. Firebase Auth for users. 6. Cloud Storage for media. 7. Security rules for access control. 8. Cloud Functions for backend logic. Use Firebase SDK v9 modular approach and batch writes for transactions.

Implement real-time detection with YOLO. Setup: 1. Choose YOLO version (v8, v9, v10). 2. Pre-trained COCO weights. 3. Inference on images/video. 4. Bounding box detection. 5. Class confidence scores. 6. Non-max suppression. 7. Custom dataset training. 8. Export to ONNX for deployment. Use Ultralytics library and implement tracking for video streams.

Enable real-time features with Socket.io. Implementation: 1. Server-side io instance. 2. Client-side connection. 3. Emit and on for events. 4. Rooms for group messaging. 5. Broadcasting to multiple clients. 6. Acknowledgements for reliability. 7. Middleware for authentication. 8. Automatic reconnection. Use with Redis adapter for scaling across servers and implement presence detection.

Build with Supabase as backend. Features: 1. PostgreSQL database with REST API. 2. Auto-generated APIs from schema. 3. Authentication (email, OAuth, magic links). 4. Row-level security policies. 5. Real-time subscriptions. 6. Storage for files. 7. Edge functions for serverless. 8. TypeScript SDK. Use supabase.from() for queries and implement triggers for complex logic.

Implement WebSocket for real-time features. Use cases: 1. Chat applications. 2. Live notifications. 3. Collaborative editing. 4. Live data dashboards. 5. Gaming multiplayer. 6. Stock tickers. Implementation: Establish connection, send/receive messages, handle disconnect, reconnect logic, heartbeat/ping-pong, scale with Redis pub/sub, authentication at connection. Use Socket.io or native WebSocket API.