We’re evaluating bi-directional data sharing patterns between Snowflake and SAP Business Data Cloud for our reporting infrastructure. Currently using traditional ETL with 4-hour batch windows, but considering zero-copy sharing to reduce latency.
The challenge is understanding the real-world tradeoffs between real-time access versus ETL latency, especially for ad-hoc reporting workloads. We need data flowing both directions - SAP BDC operational data into Snowflake for analytics, and Snowflake aggregated metrics back to SAP BDC for operational dashboards. Governance and security controls are critical since we’re dealing with customer PII.
Has anyone implemented bi-directional sharing at scale? What are the actual latency improvements versus the governance complexity? Our reporting agility is suffering with current batch delays, but we can’t compromise on data security controls.
Good point about schema evolution. How do you handle PII masking with shared data? We currently mask in the ETL layer but with direct sharing that control point disappears. Are you using Snowflake’s dynamic data masking on the shared views?
One thing to watch - bi-directional sharing creates circular dependencies that can be tricky for change management. When you update schemas on either side, you need to coordinate with consumers. We’ve had incidents where a column rename in Snowflake broke SAP BDC dashboards because the shared view wasn’t updated in sync. Version your shared objects and use change data capture patterns.
After running bi-directional sharing between Snowflake and SAP BDC for over a year across 40+ shared datasets, I can provide some concrete insights on all three focus areas.
Zero-Copy Data Sharing Reality:
The technology works brilliantly from a performance standpoint. Our data latency dropped from 4-hour ETL windows to 5-15 minute freshness depending on metadata refresh intervals. For ad-hoc reporting, this is transformational - analysts can now query near-real-time operational data for customer behavior analysis. However, ‘zero-copy’ doesn’t mean zero cost. You still pay for compute on both sides, and cross-cloud sharing (if your SAP BDC and Snowflake are in different clouds) incurs data transfer charges. Budget for 15-20% higher compute costs due to more frequent queries.
Real-Time Access vs ETL Latency Tradeoffs:
The latency improvement is real, but you trade predictability for freshness. With ETL, you know exactly when data arrives and can schedule dependent processes. With sharing, data appears continuously but you lose that orchestration control. We solved this by implementing change data capture streams on shared tables to trigger downstream processes. For reporting agility, the win is huge - business users can answer questions in minutes instead of waiting for next day’s batch. The downside is query performance variability since you’re hitting live operational systems. We implemented result caching and materialized views on the consumer side to buffer this.
Governance and Security Controls:
This is where bi-directional sharing gets complex. You need governance frameworks on both platforms that align. Our approach:
- Classify all data at source (PII, confidential, public) using tags
- Apply Snowflake masking policies and row access policies before sharing
- Create shared secure views, never share raw tables
- Implement comprehensive audit logging on both sides using Snowflake’s ACCESS_HISTORY and SAP BDC’s audit tables
- Use separate shares for different consumer groups with distinct access levels
- Document data lineage explicitly since it’s not captured automatically in sharing scenarios
For PII specifically, we mask at the view layer with policies that follow the data through shares. The SAP BDC side requires similar masking before sharing back to Snowflake. The governance complexity is real - you’re managing security in two systems that need to stay synchronized.
Bottom line: If reporting agility and data freshness are your priorities and you can invest in proper governance infrastructure, bi-directional sharing delivers significant value. If your current ETL is working and governance is already stretched thin, the operational complexity may outweigh the latency benefits. We saw 60% improvement in analyst productivity but needed two additional data governance FTEs to manage it properly.
The governance aspect is actually cleaner with sharing than ETL in my experience. With ETL you’re copying data and managing security in two places. With sharing, you define access controls once at the source and they’re enforced automatically. The tradeoff is you need robust monitoring because you can’t easily audit what queries consumers are running against shared data without proper logging infrastructure.
We implemented exactly this pattern six months ago. Zero-copy sharing reduced our data availability from 4 hours to under 10 minutes for most datasets. The key is understanding that ‘real-time’ is relative - there’s still metadata refresh cycles and query execution time. For ad-hoc reporting, the latency improvement is dramatic because analysts can query current data instead of yesterday’s batch.