Resolving Silent Data Loss in SAP HANA Cloud Analytics Through End-to-End Root Cause Analysis
Customer Context
The customer operates a large SAP landscape where SAP HANA Cloud–based analytical models are used for operational and planning reports. These analytics are business-critical and are consumed by downstream reporting tools that support daily business decision-making. The analytical layer is built on HANA Virtual Data Models and relies on SLT-based real-time data replication from the source systems, along with multiple dependent SAP tables.
Business Challenge
In January 2026, business users reported a major discrepancy in an analytics report backed by a HANA Supply Planning Allocation Model. While the expected record count was more than 80,000, the actual output showed only around 320 records. This discrepancy raised serious concerns regarding reporting accuracy, trust in analytics, and the potential impact on operational planning. At first glance, the issue appeared to be a defect in the analytical data modeling layer.
Analytics Architecture Overview
The affected analytical model was designed as a Supply Planning Allocation Virtual Data Model that consumed a Planned Supply Receipt Virtual Data Model and joined it with multiple SAP tables, including RESB, MARC, and other dependent tables. Given this layered architecture, the root cause could potentially exist in the analytical logic, join conditions, or upstream data replication processes.
Investigation Approach
A layer-by-layer diagnostic approach was followed, starting from the analytics layer and progressively moving upstream toward data ingestion and replication.
Step 1: Analytics Layer Validation
The calculation view was debugged directly in SAP HANA Cloud. During this analysis, it was observed that the RESB table contained very few records in HANA, whereas the same table in the source system, Business Suite on HANA, showed a healthy and complete dataset. This confirmed that the analytical logic and join conditions were correct and that the issue was not functional in nature. The focus therefore shifted to data ingestion and replication.
Step 2: Replication Layer Analysis
The next step involved examining the SLT-based replication layer. In the SLT replication status transaction (LTRC), it was observed that replication for the RESB table was inactive and the table was missing from the list of actively replicated objects. As an immediate corrective action, replication for the RESB table was re-initiated. Once replication resumed, the analytical model immediately reflected the correct record count in HANA Cloud. Although this resolved the symptom, it did not explain why the issue occurred in the first place.
Step 3: Root Cause Identification
To identify why replication had stopped, SLT internal logs were analyzed, focusing on the IUUC_RS_ORDER table. The analysis revealed that replication for the RESB table had been stopped on an earlier date, and the stop action was triggered by a system user ID, indicating a system-originated termination rather than a manual action. Correlating this timeline with infrastructure incident logs revealed that an unexpected production shutdown had occurred on the same date. During this shutdown, the replication process stopped silently, and no alerts or notifications were generated to indicate the failure.
Why the Issue Appeared Late
Although replication stopped several months earlier, the issue became visible only in January 2026. This delay occurred because historical data that had already been replicated into HANA Cloud remained available, allowing reports to continue showing results for weeks. However, any new records created after the replication stoppage were never replicated, and records deleted in the source system were removed due to base-view dependencies. Over time, the analytical dataset gradually eroded until the data loss became significant enough for business users to notice.
Validation Through Historical Data
To conclusively confirm the root cause, a historical data analysis was performed using daily backup data provided by Azure Data Factory. Record counts were compared from the date on which table replication failed through January 31, 2026. The analysis showed that the record count decreased steadily on a day-by-day basis. By the end of January, only around 160 records remained, which aligned precisely with business observations and confirmed the root cause beyond doubt.
Final Root Cause Summary
The RESB table replication stopped on a prior date due to an unexpected production shutdown. Because no replication monitoring or alerting was configured for the table, the failure went unnoticed. The analytics layer continued to consume stale historical data, and the issue surfaced only after significant data degradation had occurred.
Solution Implemented
To prevent recurrence, proactive monitoring measures were implemented. Automated replication monitoring was enabled in the SLT system, and email alerts were configured to trigger whenever table replication stops or the replication status becomes inactive. This ensures early detection of replication failures before they impact analytics consumers.
Business Impact
These measures restored confidence in analytics reports, prevented future silent data loss, and significantly improved the operational reliability of SAP HANA Cloud analytics. Additionally, stronger monitoring governance was established across the analytics landscape.
Key Learnings
This case highlighted that analytics issues often originate outside the analytics layer itself and that replication health is just as critical as calculation view design. Silent failures can remain undetected for extended periods, making monitoring and alerting essential components of any production-grade analytics architecture.
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