Monthly work summary for 2026-04 focusing on delivering high-value features to enable faster, more scalable data visualization in CrateDB. This month’s work centers on a major visualization performance enhancement via a new downsampling path, along with collaboration and benchmarking to validate scalability across large time-series datasets. Key outcomes: - Feature delivered: LTTB Downsampling for CrateDB Visualizations implemented as a CrateDB aggregation function, enabling efficient downsampling of large time-series queries while preserving visual fidelity. - Collaboration: Co-authored work with Bing on the LTTB implementation, ensuring robust design and shared ownership. - Initial implementation anchored in repo crate/crate, with commit 824dc0b8fb7a487bc62187a6399ce4292e148199 detailing the LTTB UDF-based approach and methodology. - Validation approach: Early benchmarks demonstrate significant reduction of data points (from 1M to 100) with practical performance results, highlighting the potential for faster dashboards and lower memory/compute costs. - Roadmap alignment: Establishes a scalable, accurate downsampling path that can be extended to additional visualizations and dashboards. Impact: This feature directly improves the end-user experience for CrateDB dashboards by delivering faster, more responsive visualizations on large datasets, enabling analysts to explore data trends without sacrificing fidelity. It also creates a reusable pattern for future data-reduction techniques within CrateDB. Technologies/skills demonstrated: CrateDB aggregation function development, in-memory data buffering strategy for accurate downsampling, performance benchmarking on large datasets, collaborative code ownership (co-authored work).