2026 · intermediate
Proactive Batch Observability: Automate File Freshness and SLA Monitoring
Modern observability focuses on APIs and microservices, yet many business-critical workflows still depend on batch jobs and file-based data pipelines. When these systems fail, they often do so silently only becoming visible after business impact.
This talk introduces a practical, tool-agnostic approach to batch observability by applying Site Reliability Engineering (SRE) principles. The core idea is simple but powerful:
In batch systems, data freshness is the equivalent of latency in real-time systems.
If data is late or missing, the system is effectively down from a business perspective.
Attendees will learn how to model batch workflows using event-driven observability, define expected vs. actual delivery SLAs, and measure reliability using a good event vs. total event SLI approach. These signals can then be translated into meaningful SLOs aligned with business outcomes.
The session also includes a brief real-world walkthrough demonstrating how these principles can be implemented to track end-to-end workflows, detect delays proactively, and provide a single, business-aligned view of reliability.
Why Attend
If your systems rely on batch jobs, scheduled workflows, or file transfers, this talk will help you:
- Make batch systems observable and measurable
- Detect issues before business impact occurs
- Move beyond job-level monitoring to true reliability measurement
- Apply SLI/SLO thinking to non-real-time systems
Key Takeaways
- Model data freshness as a reliability signal (SLI)
- Define and enforce SLAs based on data delivery
- Connect technical signals to business impact
- Apply a tool-agnostic framework across any observability stack