Zero-Data-Loss Disaster Recovery for Hybrid Financial ERP Landscapes: An AI-Driven Approach

Abstract

Enterprise financial landscapes operating across cloud-native ERP platforms, centralized finance hubs, and legacy source systems face compounding disaster recovery challenges that conventional backup-and-restore strategies are fundamentally inadequate to address. The tight operational dependencies introduced by real-time replication architectures—where a centralized universal journal continuously ingests financial postings from multiple heterogeneous source systems—demand a Zero-Data-Loss posture that is technically enforced rather than aspirationally stated. Synchronous HANA System Replication, with all financial core nodes, achieves full durability by writing every committed transaction durably until the application acknowledges completion. This gives RPO = 0 and minute-level recovery times driven by cluster automation․ STONITH fencing and cluster management automation. eliminate split-brain across the database tier and the ABAP central services layer. Enqueue Replication Server communication patterns implement application lock table persistence. Safe shutdowns, deterministic message tracking-aware replay, and the recovery-side interface framework replay are required to prove that the central journal contains exactly the source transactions and is sequentially consistent. Artificial Intelligence for observability tools aggregates telemetry across replication health, queue levels, and interface. error counts and network latency․ These are then analyzed by continuous anomaly detection in order to detect ZDL risk conditions before they impact services․ Runbooks informed by large language models provide policy guardrails against harmful failover automation decisions․ Continuous post-recovery universal journal reconciliation produces auditable evidence of recovery completeness, transforming disaster recovery from a documented contingency into a continuously verified operational guarantee for financial systems of record.