Databricks Migration Case Studies

As enterprises grapple with exploding data volumes, AI demands, and legacy platform limitations in 2025, Databricks Migration to the lakehouse architecture has become a go-to strategy. Built on Apache Spark, Delta Lake, and Unity Catalog, Databricks unifies data engineering, analytics, and machine learning—often slashing costs by 30–76% while boosting performance. But success hinges on meticulous planning: from workload audits to zero-downtime pilots. Drawing from recent Databricks Migration case studies (primarily 2024–2025), this post dives into five detailed examples of migrations from Snowflake and other legacy data warehouses. Each highlights challenges, strategies, outcomes, and lessons—offering a blueprint for your own journey. Whether you’re escaping Snowflake’s compute silos or modernizing on-prem EDWs, these stories underscore Databricks Migration’s edge in scalability and TCO.

Case Study 1: GetYourGuide – From Snowflake Silos to Unified BI (Travel Tech, 2025)

Background and Challenges: GetYourGuide, a global travel experiences platform, relied on Snowflake for BI analytics via Looker, processing customer and supplier data for real-time insights. By early 2025, fragmented data sources led to duplicated pipelines and operational costs ballooning 25% YoY, exacerbated by Snowflake’s separation of compute and storage for BI-only workloads. Databricks Migration Shadow ETL processes in Airflow compounded maintenance overhead, delaying AI-driven personalization features.

Migration Strategy: The team adopted a phased, zero-downtime approach over 3 months:

• Assessment (Week 1-2): Profiled 50+ Snowflake tables using account_usage views to prioritize high-velocity BI datasets (e.g., booking events).
• Data Movement (Month 1): Exported tables to Parquet via Snowflake’s COPY INTO S3, then ingested into Delta tables on Databricks using Auto Loader for incremental syncs. Apache Airflow workflows were refactored into Databricks Workflows for orchestration.
• Transformation and Testing (Month 2): Converted SQL views to Delta Live Tables (DLT) for automated pipelines, ensuring ACID compliance. A daily validation workflow compared row counts and sample records between source and target.
• Cutover (Month 3): Switched Looker data sources to Databricks SQL warehouses, with parallel runs for 2 weeks to monitor query latency.

Outcomes and Metrics: Achieved 20% OpEx reduction through consolidated storage ($0.023/TB-month on Delta vs. Snowflake’s tiers) and 15% faster Looker queries via Photon engine optimization. BI accuracy improved 10% with Unity Catalog’s governance, enabling real-time supplier dashboards. Total migration cost: ~$150K, ROI in 6 months via efficiency gains.

Lessons Learned: Hybrid validation pipelines are crucial for BI-heavy migrations—GetYourGuide’s Airflow-to-DLT shift cut orchestration bugs by 40%. Pro tip: Leverage Databricks’ Partner Connect for seamless Looker integration early.

Case Study 2: What If Media Group (WIMG) – Streaming-First Overhaul from Snowflake (Marketing Tech, 2023-2025 Update)

Background and Challenges: WIMG, a digital marketing firm, used Snowflake for event scoring in messaging pipelines, handling billions of user interactions. By 2023, full-table reloads every 2 minutes drove costs up several million annually, with latencies hitting 30+ seconds—limiting real-time personalization for clients. Snowflake’s batch-oriented model couldn’t scale to streaming without third-party crutches.

Migration Strategy: A 4-month streaming-centric Databricks migration:

• Discovery (Month 1): Audited pipelines with Snowflake’s query history to identify bottlenecks (e.g., 80% compute on joins). Piloted a subset of dimensional tables (user profiles) on Databricks.
• Architecture Shift (Month 2): Migrated key tables to Delta Lake for ACID transactions, using Structured Streaming to process events from Kafka directly—bypassing Snowflake reloads. PySpark jobs replaced SQL UDFs for on-the-fly scoring.
• Integration and Scaling (Month 3): Implemented Delta Live Tables for end-to-end pipelines, with auto-scaling clusters handling 10x spikes. Data validation via Great Expectations ensured 99.9% parity.
• Production Rollout (Month 4): Phased cutover by client segment, with A/B testing on latency metrics.

Outcomes and Metrics: 76% TCO reduction on pipelines (from $millions to sub-$1M annually) via event-level processing vs. batch reloads. Latency dropped to <1 second, enabling 20% uplift in client engagement rates. Post-2025 updates added ML scoring, boosting accuracy 15%.

Lessons Learned: Streaming-first designs unlock Databricks’ Spark heritage—WIMG’s event-to-Delta joins cut waste dramatically. Key: Start with high-pain workloads; use Databricks’ cost forecasting tools for pre-migration TCO modeling.

Case Study 3: Unnamed Real Estate Client – Cost-Driven Pivot from Snowflake (Real Estate Analytics, 2025)

Background and Challenges: A mid-sized real estate firm saw Snowflake compute costs explode 3x over two years amid data growth to petabytes, driven by historical sales profiling. Materialized views and cluster keys offered temporary relief, but AI features like predictive pricing demanded unified ML—Snowflake’s silos forced tool sprawl.

Migration Strategy: 6-week computeaccelerated migration using Blueprint Technologies’ accelerator:

• Profiling (Week 1): Extracted account_usage stats to select 100GB candidate datasets; hands-on Unity Catalog workshop for governance.
• ETL Refactor (Weeks 2-3): Used Terraform for Azure setup, exporting to Parquet via S3. Converted transformations with Matillion to Databricks migrartion Partner Connect.
• Benchmarking (Week 4): Ran Lakehouse Optimizer on samples, comparing runtimes (Databricks 2x faster on ML queries).
• Deployment (Weeks 5-6): Loaded to Delta tables; validated with row counts and schema diffs. Power BI egress pipelines ensured continuity.

Outcomes and Metrics: 40% cost savings via Delta’s compression; 3x faster ML model training on house location/timestamp data. Ownership history queries dropped from hours to minutes, unlocking new revenue from predictive analytics.

Lessons Learned: Accelerators like Blueprint’s cut timelines 50%—focus on non-prod pilots first. Databricks’ migration vectorized Parquet scanner yielded 3-4x gains on analytical scans.

Case Study 4: Bayada Health – Enterprise Consolidation from Snowflake (Healthcare, 2025)

Background and Challenges: Bayada, a home health provider, juggled Snowflake with legacy systems for patient data reconciliation, facing 35% processing delays and 40% manual reconciliation efforts. Fragmented analytics hindered real-time efficiency amid 2025 regulatory pressures.

Migration Strategy: 5-month enterprise-wide shift:

• Discovery (Month 1): Automated profiler assessed workloads; prioritized reconciliation pipelines.
• Data Ingestion (Month 2): Federated exports to Delta via Auto Loader; refactored ETL to DLT.
• Governance Overhaul (Month 3): Implemented Unity Catalog for HIPAA-compliant access.
• AI Integration (Month 4): Added real-time analytics with MLflow for predictive care.
• Go-Live (Month 5): Parallel runs with 99% uptime; monitored via Databricks SQL alerts.

Outcomes and Metrics: 35% faster processing, 40% reconciliation cuts, 20% OpEx drop, and 15% efficiency boost via real-time insights. Consolidated platform reduced tool sprawl, enabling AI pilots.

Lessons Learned: Healthcare migrations thrive on governance-first approaches—Bayada’s Unity Catalog rollout prevented compliance snags. Hybrid Snowflake-Databricks migration phases eased stakeholder buy-in.

Case Study 5: Trek Bicycle – Legacy EDW to Lakehouse Modernization (Consumer Goods, 2025)

Background and Challenges: Trek’s on-prem EDW (Teradata/SQL Server hybrid) struggled with unstructured product data and ML for supply chain forecasting, incurring high hardware costs and 2x query delays during peaks.

Migration Strategy: 4-month lift-and-modernize via Databricks PS:

• POC (Month 1): Migrated one use case (inventory ETL) using BladeBridge for code conversion.
• Full Data Lift (Month 2): Parquet exports to S3; Spark jobs for z-ordering optimization.
• Modernization (Month 3): Rebuilt workflows for ML-based demand prediction with Delta Live Tables.
• Scale-Out (Month 4): Auto-scaling clusters; integrated with Tableau for dashboards.

Outcomes and Metrics: 30-40% cost savings, isolated resources for ETL/BI, and optimized queries across types. Scalability unlocked 25% faster forecasting.

Lessons Learned: Blend lift-and-shift for speed with modernization for value—Trek’s POC validated 2x performance pre-commitment.

Key Takeaways: Patterns Across Migrations

These cases reveal universal truths: Start with audits (e.g., query_history views), prioritize streaming/ML workloads for ROI, and use tools like Lakebridge for 80% automation. Challenges like data type mismatches (e.g., VARIANT to STRUCT) are common but mitigated by Delta’s schema evolution. In 2025, 60% of migrations report 2x timelines via accelerators, per Databricks migration sessions.