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Real-time dashboards that perform well in demos often falter under production conditions due to architectural bottlenecks rather than issues with the charting library itself. The problem typically arises from sending excessive data points to the browser, leading to performance degradation in terms of data handling and rendering. The solution involves understanding end-to-end latency budgets and optimizing across four key layers: data volume from the backend, serialization and network transfer, client-side parsing and memory, and rendering and interaction. Techniques such as perceptually lossless downsampling, particularly using the Largest Triangle Three Buckets (LTTB) algorithm, adaptive resolution queries, and efficient payload strategies like binary formats and incremental updates, are crucial for maintaining performance. Additionally, selecting the appropriate rendering technology—Canvas for simplicity or WebGL for scale—and leveraging Web Workers for off-main-thread processing are essential. Platforms like Tinybird offer backend infrastructure designed for real-time analytics, enabling sub-100ms queries and efficient data handling, thus eliminating the need for complex custom solutions and ensuring fast charts that maintain their performance as data scales.