The SAP HANA to Snowflake Transformation: A Value Engineering Perspective

In my time leading the data transformation at Airpremia, I encountered a challenge common to many established enterprises: the "Legacy Data Trap." The organization was running on SAP HANA, a powerful but often siloed environment where business logic was buried deep within ERP modules, making agile data science and AI initiatives nearly impossible.

This wasn't just a technical debt problem; it was a value leakage problem.

The Problem: The High Cost of "Business as Usual"

When I joined Airpremia, the data team was spending 70% of their time on "data plumbing"—manual extractions, fixing broken pipelines, and reconciling reports that didn't match.

The SAP HANA environment, while robust for transactional workloads, created several bottlenecks:

1. Siloed Logic: Critical business rules were locked inside SAP-specific views, inaccessible to modern analytics tools.
2. High TCO: The infrastructure costs for scaling HANA were prohibitive for large-scale ML workloads.
3. Low Velocity: It took weeks to answer simple cross-departmental questions (e.g., "How does aircraft maintenance schedule impact customer NPS?").

The Strategy: Architecting for Value

We didn't just want to "move" data; we wanted to transform the value journey. The migration to Snowflake was designed as the foundation for an enterprise-wide "Single Source of Truth."

1. SAP ERP Extraction Strategy

We built custom extraction pipelines that didn't just dump tables, but mapped complex SAP ERP modules into clean, business-friendly dimensions. By using a modern stack (Snowflake + dbt + Rudderstack), we moved from batch-heavy processing to a near real-time flow.

2. Establishing the "Value Infrastructure"

By centralizing data from SAP ERP, flight operations, and customer touchpoints (Braze, Statsig) into Snowflake, we created a unified data mesh. This allowed us to:

• Reduce dbt model run times by 93%.
• Automate KPI monitoring, moving from weekly manual reports to real-time Slack alerts.

The Business Impact: Beyond Efficiency

The migration wasn't an end in itself; it was the enabler for high-impact AI. Once the foundation was solid, we were able to:

• Optimize Revenue: Real-time visibility into booking trends allowed for more dynamic pricing strategies.
• Improve Customer Loyalty: By integrating Braze with Snowflake, we personalized marketing campaigns based on actual flight behavior, not just demographic data.

Lessons for Value Engineering Leaders

If you are considering a similar migration, remember these three pillars:

1. Don't Lift and Shift: Use the migration to refactor your data models. Legacy logic in SAP is often outdated; don't bring it into your new modern data stack.
2. Focus on Stakeholder ROI: At every step, show how the new architecture solves a specific business pain point. For us, it was reducing ad-hoc query requests by 40% for analysts.
3. Data Culture is the Glue: Technology alone won't fix a siloed organization. We published bi-weekly "Data Magazines" to evangelize the new capabilities and ensure every department knew how to leverage the new platform.

The move from SAP HANA to Snowflake is more than a database change—it's a strategic shift from being "data-heavy" to being "data-driven."

If your organization is struggling with legacy SAP bottlenecks, I'd love to share the specific frameworks we used to navigate this transformation.