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GitHub Skill

freshness-latency-slos

See the main Data Freshness and Latency skill for comprehensive coverage of freshness monitoring and SLO tracking.

AmnadTaowsoam☆ 0 仓库 Stars0 仓库 Forks

安装与使用

请根据来源仓库和你的 AI 工具说明安装此技能。先阅读技能文件和依赖要求,再添加到工作流。

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技能说明

Freshness Latency SLOs

This skill is covered in detail in the main Data Freshness and Latency skill.

Please refer to: 43-data-reliability/freshness-latency/SKILL.md

That skill covers:

  • Freshness vs latency distinction
  • Freshness requirements by use case
  • Measuring data freshness (SQL, Python, dbt)
  • Latency measurement (end-to-end, per-stage, P50/P95/P99)
  • Freshness monitoring and alerting
  • Watermark tracking for streaming data
  • Improving freshness (CDC, incremental updates, parallel processing)
  • Trade-offs (freshness vs cost/completeness/accuracy)
  • Freshness SLAs and SLOs
  • SLO compliance measurement
  • Tools (Monte Carlo, Grafana, dbt freshness tests)
  • Handling stale data
  • Real freshness issues and solutions

Related Skills

  • 43-data-reliability/freshness-latency (Main skill)
  • 43-data-reliability/data-quality-monitoring
  • 43-data-reliability/data-contracts

使用示例

查看来源仓库中的使用示例与配置说明。

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来源历史

收录时间
2026-01-20
索引更新时间
2026-01-26
Branch
main

收录与同步日期属于本站记录。源码修改历史请查看来源仓库。

GitHub ↗

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