
Over five months, contributed to the Accedian/grafana-docker repository by building and enhancing Grafana dashboards for Spark workloads, focusing on observability, data accuracy, and operational reliability. Delivered features such as the StitchitMetrics and FM-Alert dashboards, expanded metrics coverage, and improved CI/CD stability through Docker image updates and configuration management. Addressed post-upgrade metric misalignments and dashboard display bugs, ensuring accurate reporting and faster troubleshooting. Leveraged skills in dashboarding, monitoring, and DevOps, using technologies like Spark, Docker, YAML, and Shell scripting. Maintained clear documentation and changelogs, supporting data-driven decisions and enabling more effective monitoring for operations and product teams.
September 2025 monthly summary for Accedian/grafana-docker: Delivered targeted Grafana dashboard enhancements and bug fixes that improve observability and speed up insight for Spark workloads. Key features include Spark Dashboards Enhancements (Disk Spill, Executor Heap, Storage Heap panels) and the spark_executor_runTime metric on the Spark Resources dashboard, along with fixes that refine dashboard display and metrics reliability. Implemented Dashboard Metric Display Fixes to address recCount display on the object-type dashboard and incorrect labels on latentlag in the spark ingestion/lag dashboards, with changelogs updated accordingly. These changes improve monitoring accuracy, reduce troubleshooting time, and enable better capacity planning and SLA adherence. Technologies demonstrated include Grafana dashboard design, Spark metrics instrumentation, and changelog maintenance.
September 2025 monthly summary for Accedian/grafana-docker: Delivered targeted Grafana dashboard enhancements and bug fixes that improve observability and speed up insight for Spark workloads. Key features include Spark Dashboards Enhancements (Disk Spill, Executor Heap, Storage Heap panels) and the spark_executor_runTime metric on the Spark Resources dashboard, along with fixes that refine dashboard display and metrics reliability. Implemented Dashboard Metric Display Fixes to address recCount display on the object-type dashboard and incorrect labels on latentlag in the spark ingestion/lag dashboards, with changelogs updated accordingly. These changes improve monitoring accuracy, reduce troubleshooting time, and enable better capacity planning and SLA adherence. Technologies demonstrated include Grafana dashboard design, Spark metrics instrumentation, and changelog maintenance.
In July 2025, delivered the FM-Alert Metrics rollout for Spark ingestion and dashboards in Accedian/grafana-docker, with a data source schema update to support expanded metrics and ensure end-to-end data processing. This work enhances observability, improves alert fidelity, and provides accurate, user-visible metrics across ingestion to dashboards. Technologies demonstrated include Spark ingestion pipelines, data source schema evolution, metrics instrumentation, and Grafana dashboard integration. Impact: faster root-cause analysis, more reliable dashboards, and data-driven decision support for operations and product teams.
In July 2025, delivered the FM-Alert Metrics rollout for Spark ingestion and dashboards in Accedian/grafana-docker, with a data source schema update to support expanded metrics and ensure end-to-end data processing. This work enhances observability, improves alert fidelity, and provides accurate, user-visible metrics across ingestion to dashboards. Technologies demonstrated include Spark ingestion pipelines, data source schema evolution, metrics instrumentation, and Grafana dashboard integration. Impact: faster root-cause analysis, more reliable dashboards, and data-driven decision support for operations and product teams.
Month: 2025-03 — This period focused on delivering observable value through dashboard-driven monitoring in the Accedian/grafana-docker repository, accompanied by UI refinements to improve clarity and usability. No major bugs required remediation this month. The work emphasizes business value through enhanced visibility, faster data-driven decisions, and a cleaner user experience for Grafana dashboards.
Month: 2025-03 — This period focused on delivering observable value through dashboard-driven monitoring in the Accedian/grafana-docker repository, accompanied by UI refinements to improve clarity and usability. No major bugs required remediation this month. The work emphasizes business value through enhanced visibility, faster data-driven decisions, and a cleaner user experience for Grafana dashboards.
Month: 2024-12 - Key work focused on observability enhancements and CI/CD stability for Accedian/grafana-docker. Delivered Forecasting Dashboard Metrics Enhancement and updated the CircleCI Docker image to docker-go-sdk 0.42.0 on debianstretch, improving visibility into forecasting workloads, build reliability, and security posture. No critical bugs reported or fixed this month; maintenance work improved monitoring capabilities and CI/CD resilience.
Month: 2024-12 - Key work focused on observability enhancements and CI/CD stability for Accedian/grafana-docker. Delivered Forecasting Dashboard Metrics Enhancement and updated the CircleCI Docker image to docker-go-sdk 0.42.0 on debianstretch, improving visibility into forecasting workloads, build reliability, and security posture. No critical bugs reported or fixed this month; maintenance work improved monitoring capabilities and CI/CD resilience.
Month: 2024-11 — Accedian/grafana-docker: Delivered stability improvements and data accuracy post-Spark upgrade. No new features delivered this month; primary focus on bug fix and alignment of metrics in the Record Count Panel to ensure reliable reporting for stakeholders and operators.
Month: 2024-11 — Accedian/grafana-docker: Delivered stability improvements and data accuracy post-Spark upgrade. No new features delivered this month; primary focus on bug fix and alignment of metrics in the Record Count Panel to ensure reliable reporting for stakeholders and operators.

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