How a Major Airline Modernized 250+ Workflows with Cloud-Native Platforms and Zero Disruptions

Client Overview

The client is a major U.S.-based airline operating an extensive network of domestic and international routes. Like many enterprise organizations, the client’s data ecosystem had grown around legacy tools, most notably Alteryx, which powered hundreds of workflows critical to revenue management, flight operations, and customer analytics.

However, as the demand for real-time insights, advanced analytics, and AI capabilities increased, the Alteryx-based architecture became a limiting factor. The organization needed a scalable, cost-efficient data stack that could support both technical users and business analysts, without disrupting existing operations.

To meet these goals, the airline partnered with v4c.ai to migrate its data workflows to a modern cloud-native architecture using Databricks and Dataiku.

The Challenge

As part of a broader modernization initiative, the client aimed to retire its legacy Alteryx environment. However, the existing platform was deeply embedded in critical operations, including revenue forecasting, flight operations, and customer analytics, making the transition both high-risk and high-stakes.

• Scale and Variability: Over 250 Alteryx workflows powered essential decision-making across teams. These varied significantly in complexity, with some containing hundreds of interdependent components. Any misstep in migration risked disrupting downstream reporting and operational workflows.
• Unclear Platform Fit: Different business users had different needs, ranging from machine learning workflows to highly collaborative, low-code development. Selecting between Databricks and Dataiku for each workflow required careful analysis of use cases, performance demands, and user personas.
• Need for Zero Downtime: The organization could not afford production interruptions. All workflows had to be migrated, tested, and deployed without impacting daily operations or delaying business reports.
• Limited Internal Expertise on Target Platforms: While the teams were well-versed in Alteryx, they lacked hands-on experience with Databricks and Dataiku, raising the stakes for enablement, training, and long-term maintainability.

The v4c.ai Approach

To help the client exit their Alteryx dependency without impacting daily operations, v4c.ai designed and executed a structured, automation-led migration program. The approach combined deep workflow analysis, platform-fit evaluation, engineering automation, and stakeholder enablement, all delivered within a hybrid Databricks–Dataiku architecture tailored to varied user needs.

At the heart of the strategy was a five-phase framework that balanced speed, accuracy, and long-term maintainability:

Phase 1: Migration Discovery

Objective: Assess the Alteryx landscape and define migration pathways

• Conducted full inventory of workflows, SQL objects, and dependencies
• Applied profiler-based workload analysis and performance benchmarking
• Scored each workflow for migration complexity and platform alignment
• Identified high-value use cases and business-critical processes

Phase 2: Project Refactoring

Objective: Eliminate inefficiencies and map to cloud-native design patterns

• Mapped Alteryx components to Databricks or Dataiku equivalents
• Planned workflow-level optimization opportunities
• Used platform advantage analysis to define conversion logic
• Created a checklist-based framework for migration readiness

Phase 3: Automated Migration

Objective: Migrate at scale with automation and validation

• Applied v4c.ai’s migration accelerators to automate 70% of workflow conversion
• Executed custom code generation where needed for platform-specific logic
• Built a validation engine to test feature parity and data integrity
• Tuned workflows for improved runtime performance on new platforms

Phase 4: Training & Enablement

Objective: Accelerate adoption and reduce transition friction

• Launched a Hub-and-Spoke Enablement Model to train core users and scale adoption
• Delivered a Citizen Data Science Program (CDSP) to empower non-technical users
• Conducted platform-specific training on Databricks and Dataiku best practices
• Shared reusable templates and documentation for ongoing development

Phase 5: Change Management

Objective: Drive long-term platform adoption and continuous improvement

• Defined success criteria and tracked adoption metrics
• Identified platform champions and created a support framework
• Established feedback loops for iterative improvements post go-live

Business Outcome:

The migration program delivered measurable performance, efficiency, and operational gains, without disrupting day-to-day business processes. Key outcomes included:

• 250+ Workflows Successfully Migrated: All workflows were assessed, refactored where needed, and transitioned to cloud-native platforms with full validation and performance tuning.
• 70% Automation in Workflow Conversion: Leveraging v4c.ai’s migration accelerators significantly reduced manual rework, accelerated delivery timelines, and ensured consistency across environments.
• 40% Faster Workflow Execution: Workflows migrated to Databricks and Dataiku demonstrated improved processing times, supporting faster analytics and decision cycles across business units.
• Zero Business Disruption: All production processes continued uninterrupted during the migration. No delays, outages, or regression incidents were reported.
• Increased Platform Flexibility and Cost Efficiency: By adopting a hybrid architecture, the client optimized workload placement, using Databricks for compute-intensive operations and Dataiku for collaborative projects, resulting in better resource utilization and future-ready scalability.
• Improved Data Literacy and Team Enablement: Targeted training programs accelerated user onboarding and empowered analysts and citizen developers to adopt new platforms confidently.

Conclusion

By moving away from legacy Alteryx infrastructure, the client gained a faster, more flexible, and scalable data environment. v4c.ai delivered an end-to-end migration with a strong focus on automation, platform fit, and user adoption, ensuring that day-to-day operations were never disrupted.

With Databricks and Dataiku in place, teams can now build, run, and manage workflows more efficiently while supporting future analytics and AI initiatives. This project reflects v4c.ai’s ability to plan, execute, and operationalize large-scale platform changes with speed, accuracy, and minimal risk.

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