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Why Mosaic? | Mosaic

idl.uw.edu · 849 words · saved by 1 readers

Though many expressive visualization tools exist, scalability to large datasets and interoperability across tools remain challenging. The visualization community lacks open, standardized tools for integrating visualization specifications with scalable analytic databases. While libraries like D3 embrace Web standards for cross-tool interoperability, higher-level frameworks often make closed-world assumptions, complicating integration with other tools and environments. Visualization tools such as ggplot2, Vega-Lite / Altair, and Observable Plot support an expressive range of visualizations with a concise syntax. However, these tools were not designed to handle millions of data points. Mosaic provides greater scalability by pushing data-heavy computation to a backing DuckDB database. Mosaic improves performance further by caching results and, when possible, performing automatic query optimization. The figure below shows render times for static plots over increasing dataset sizes. Mosaic p

Why Mosaic? | Mosaic Skip to content Menu Return to top Are you an LLM? You can read better optimized documentation at /mosaic/why-mosaic.md for this page in Markdown format Why Mosaic? ​ Though many expressive visualization tools exist, scalability to large datasets and interoperability across tools remain challenging. The visualization community lacks open, standardized tools for integrating visualization specifications with scalable analytic databases. While libraries like D3 embrace Web standards for cross-tool interoperability, higher-level frameworks often make closed-world assumptions,

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