
S. Thobi Sinaga developed a Unique Trace Count Display for the opensearch-project/dashboards-observability repository, focusing on enhancing trace visibility and metric accuracy within observability dashboards. Leveraging React and TypeScript, Sinaga implemented cardinality aggregation to accurately display the total number of unique traces, refactoring both data handling and UI components to support this feature. The work included updating warning messages and improving pagination, which contributed to a more robust and user-friendly experience. By addressing trace count accuracy and streamlining the interface, Sinaga enabled faster root-cause analysis and improved mean time to resolution, demonstrating depth in frontend and data visualization engineering.

May 2025 monthly summary for opensearch-project/dashboards-observability: Delivered Unique Trace Count Display using cardinality aggregation, with refactored data handling and UI components reflecting total unique traces. Updated warning messages and pagination to improve robustness and user experience. This work enhances trace visibility, metric accuracy, and troubleshooting speed, delivering clear business value for observability dashboards. Demonstrated skills in data modeling, UI/UX integration, and performance-conscious refactoring.
May 2025 monthly summary for opensearch-project/dashboards-observability: Delivered Unique Trace Count Display using cardinality aggregation, with refactored data handling and UI components reflecting total unique traces. Updated warning messages and pagination to improve robustness and user experience. This work enhances trace visibility, metric accuracy, and troubleshooting speed, delivering clear business value for observability dashboards. Demonstrated skills in data modeling, UI/UX integration, and performance-conscious refactoring.
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