How Raiffeisen Bank International Cut Analytics TCO by 5x with Databricks SQL
Raiffeisen Bank International (RBI), one of Central and Eastern Europe's largest banking groups, migrated its fragmented analytics estate across multiple countries to Databricks SQL. The result was a 3–4x improvement in average SQL query performance, 30–40% faster time to insight for analysts, and a 5x reduction in analytics total cost of ownership compared to its previous cloud solution.
Impact
3–4x
Average SQL query performance improvement
30–40%
Faster time to insight
5x
Analytics TCO reduction vs previous cloud
30 days → 12 minutes
Extreme query time reduction
Challenge
RBI's analytics environment was fragmented across dozens of banks and departments, each with its own SQL conventions, access controls, and cloud systems — making cross-team collaboration difficult, governance inconsistent, and analytics costs difficult to control at group level.
Solution
RBI deployed Databricks SQL as a unified analytics foundation across the group via a phased migration that prioritized governance and change management, enabling elastic compute at scale, centralized cost monitoring, and open-format data access that meets European banking compliance requirements.
Tools & Technologies
What Leaders Say
“Migration is not a technical problem — it's onboarding, governance, and convincing people that the platform is safe and reliable.”
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Full Story
Raiffeisen Bank International operates a highly federated network of banking subsidiaries across Central and Eastern Europe, serving retail, corporate, and institutional customers under stringent regulatory requirements for security, governance, and auditability. Over decades of organic growth and acquisition, the group accumulated a fragmented analytics environment: each bank and department had built its own SQL conventions, access controls, and operational models. Collaboration across teams was difficult, code reuse was limited, and there was no consistent way to govern or audit data usage across the group.
The result was a patchwork of on-premises and cloud systems, each with different dialects and operational models. As George Moldovan, the data leader who oversaw the transformation, described it: legacy SQL conventions and systems developed over time had become insufficiently strong, unified, or modern enough to support the data- and AI-driven demands of contemporary banking. The gap between what the analytics infrastructure could deliver and what risk, compliance, finance, and retail teams needed was widening.
RBI chose Databricks SQL as the shared analytics foundation for a new internal platform called APEX. Rather than a risky big-bang cutover, the team ran legacy platforms and Databricks in parallel, onboarding users gradually, validating results, and protecting production workloads throughout. Moldovan was clear that migration is fundamentally a change management problem — onboarding, governance, and building trust in the new platform — not a technical one. The team invested accordingly.
Once workloads began running on Databricks SQL, the performance improvements were immediate. Queries that had constrained analysts due to long runtimes became interactive. In one extreme case, a query that took 30 days on the legacy infrastructure completed in 12 minutes on Databricks. Average workloads now run 3–4x faster than on the previous cloud solution. Elastic compute allows teams to scale resources on demand for large analytical jobs, supporting hundreds of concurrent users across risk, compliance, and finance without performance degradation.
Cost improvement came from architectural simplification rather than usage restrictions. By consolidating analytics onto Databricks SQL and serving BI workloads directly from the platform, RBI significantly reduced unnecessary data movement — historically one of the largest drivers of cloud analytics cost. The bank built its own cost-monitoring and forecasting platform on Databricks to track usage by warehouse, user, and workload. The resulting TCO is 5x lower than the previous cloud solution. For a European banking group, governance and open standards were equally important: Databricks' support for open formats and APIs ensures that RBI's data foundation remains portable and regulatorily transparent as requirements continue to evolve.
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