Enabling retail agility through Informatica-to-Databricks modernization

Discover how FulkrumCloud automated Informatica-to-Databricks ETL conversion, embedding validation and governance while delivering a future-ready Lakehouse architecture designed for retail innovation.

3-min read

Overview

The leading automotive company is one of the largest retail enterprises in the U.S., relying on high-performance data systems to power decision-making across merchandising, supply chain, and customer experience. However, its legacy Informatica estate, spanning thousands of ETL jobs across multiple operating units, had become a bottleneck to agility. Performance limitations, manual testing cycles, and escalating infrastructure costs constrained scalability and slowed innovation.

To accelerate its cloud-first transformation and embrace Databricks as the foundation for real-time analytics, Organization needed to modernize its ETL ecosystem. FulkrumCloud automated the Informatica-to-Databricks conversion, embedding validation and governance while delivering a future-ready Lakehouse designed for AI-driven retail innovation.

Key Challenges

  • High-cost, slow-to-change Informatica environment spanning thousands of legacy ETL jobs across business units
  • Performance bottlenecks and manual QA cycles leading to slow releases and inconsistent data quality
  • Regulatory and compliance risk due to limited lineage and observability across pipelines
  • Rising infrastructure costs from proprietary licensing and aging on-premises hardware
  • Migration risk amplified by reliance on scarce legacy skillsets

Solution

For this leading automotive company, modernization was not just a technical upgrade, but a strategic move toward retail agility. The enterprise needed to accelerate Databricks adoption while ensuring reliability, governance, and compliance.

Here’s how FulkrumCloud leveraged the Skybridge FrameworkTM to make it possible:

  • Prioritize what matters (CloudSense): Assessed company’s Informatica job inventory, mapping complexity and dependencies to enable risk-based migration planning and identify reusable transformation logic across 47 subject areas.
  • Automate the heavy lifting (CloudForge): AI-driven accelerators converted Informatica mappings into Databricks-native PySpark pipelines, automating code generation, parameterization, and optimization (partitioning, caching, orchestration redesign).
  • Validate outcomes (CloudSure): FulkrumCloud’s validation engine automated row- and schema-level reconciliation to ensure near-perfect accuracy at cutover. AI copilots generated test cases, documentation, and validation reports, leveraging AI prompt libraries and synthetic data generation to perform shift-left testing and ensure early inefficiency detection.

Impact

Accelerated modernization timelines

FulkrumCloud’s automation and AI-driven conversion capabilities enabled the organization to migrate thousands of Informatica workloads to Databricks up to 30–40% faster than manual baselines, shortening delivery cycles, improving forecast accuracy, and accelerating business value realization.

Enterprise-scale transformation

The engagement is focused on managing 29,000+ ETL objects across 47 subject areas, using risk-based migration planning to prioritize workloads and ensure predictable modernization across organization’s large, distributed data landscape.

Uncompromised accuracy and trust

FulkrumCloud’s AI-powered intelligence capabilities enabled the validation of 3,100+ mappings and 2,700+ workflows, achieving over 98% cutover accuracy through row-level and schema-level validation, ensuring data parity and reliable production readiness.

Faster testing and deployment

Automated, AI-generated test cases reduced testing cycles by 25% and cut manual QA effort by 90%, delivering faster release velocity and dependable validation at scale. Seamless CI/CD integration ensured consistent adoption and enterprise enablement.

Financial impact through cost optimization

The modernization eliminated legacy licensing and hardware costs, consolidating workloads into a unified, cloud-native ecosystem that lowered TCO and simplified operations.

A governed, AI-ready Lakehouse foundation

The modernized Databricks Lakehouse delivered built-in lineage, auditability, and performance enhancements, integrated with prebuilt accelerators for AWS, Azure, and GCP, creating a governed, future-ready data ecosystem that positions the organization for AI-driven retail innovation.

Insight-driven retail decisions

With clean, reconciled, and analytics-ready data on Databricks, the modernization paves the way for more granular insights that strengthen company’s ability to drive timely, personalized engagement and support long-term improvements in customer loyalty.

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Enterprise-Scale Informatica-to-Databricks Transformation for Retail | FulkrumCloud Case Study