Grab built an automated system called Data Production Issues (DPIs) that detects when certified data products fail their contracts, diagnoses root causes without manual intervention, and fixes common failures automatically. The system keeps data quality issues actionable by filtering noise, assigning problems to the right owner, and closing issues only when the underlying data actually recovers.

• DPIs use a standardized Data Health API with four error categories (upstream, platform, job, data) to route failures to the correct team without requiring platform-specific knowledge, enabling consistent incident management across Grab's heterogeneous data systems.
• The triage phase deduplicates alerts and groups related failures by root cause, preventing responders from drowning in repetitive notifications while ensuring only real contract breaches become operational issues.
• Hugo, Grab's ingestion platform, consolidates signals from multiple sources into a three-layer diagnosis workflow that automatically identifies root causes and recommends fixes, eliminating manual log inspection and shortening remediation time.

Writing SQL queries is straightforward, but creating a reliable system for running them efficiently is complex and often results in poor data quality and operational inefficiencies. Transitioning from ad-hoc scripts to a structured, spec-driven architecture enhances reproducibility, validation, and observability of SQL jobs, ultimately leading to better management of data and costs.

• Ad-hoc SQL scripts scale poorly because they lack reproducibility, validation, and observability, leading to silent data quality issues and wasted compute costs.
• Moving to a spec-driven architecture (defining queries as versioned, declarative configs rather than one-off scripts) makes jobs testable, auditable, and easier to rerun consistently.
• Centralizing query execution through a structured system enables built-in validation and monitoring, catching errors before they propagate downstream.