Technical Review Article: Self-Healing Lakehouse Manifests

Abstract

Self-Healing Lakehouse Manifests represents a transformative advancement in data reliability engineering for modern enterprise architectures. This innovation addresses a critical gap in lakehouse platforms where underlying object store inconsistencies can compromise data availability and integrity. The system introduces an autonomous control plane that continuously monitors transaction logs and file manifests, detecting discrepancies through cryptographic verification and predictive analytics. When inconsistencies are detected, the architecture orchestrates targeted repair operations while maintaining concurrent query access through sophisticated isolation mechanisms. The multi-layered design incorporates real-time change detection, Merkle tree-based verification, Bayesian drift prediction, and atomic repair operations that preserve transactional integrity. Implementation follows a carefully structured roadmap that minimizes operational risk while delivering incremental value. The architecture demonstrates exceptional resilience across diverse failure scenarios including network partitions, throttling events, and schema evolution complexities. By transforming traditionally reactive failure response into proactive, autonomous maintenance, Self-Healing Lakehouse Manifests elevates data lakehouses to enterprise-grade reliability status suitable for mission-critical applications without compromising the flexibility and scalability advantages inherent in modern data architectures.