Accelerated Legacy ETL Modernization and Databricks Migration for a Fortune 500 Retailer Through AI-First Automation

A Fortune 500 retailer with over $100 billion in annual revenue sought to modernize its data management architecture to support business expansion and future growth initiatives. Its existing ETL ecosystem consisted of numerous legacy Informatica workloads, complex point-to-point integrations, and on-premises dependencies that created operational inefficiencies and limited scalability. To align with a domain-driven data strategy and establish a cloud-native foundation, the organization partnered with Bitwise to accelerate migration to Databricks. The objective was to simplify the data landscape, reduce technical debt, and create a modern architecture capable of supporting faster data consumption, analytics, and AI-driven innovation.

Client Challenges and Requirements

• Legacy point-to-point integrations created a complex and difficult-to-maintain data ecosystem, limiting scalability and increasing operational overhead.
• Redundant ETL processes and duplicate data engineering efforts reduced efficiency and increased maintenance costs.
• Heavy dependence on on-premises source systems introduced modernization challenges and constrained future data platform evolution.
• Large-scale migration planning and inventory assessment required significant manual effort, increasing project timelines and execution risks.
• The organization needed a scalable Databricks-based architecture aligned with its domain-driven data strategy and long-term growth objectives.

Bitwise Solution

• Conducted an automated technical assessment using Fulkrum AI to analyze workloads, classify migration candidates, and establish data-driven modernization roadmaps.
• Leveraged AI-assisted code conversion capabilities to transform legacy Informatica workloads into Databricks PySpark using standardized migration patterns and reusable libraries.
• Re-engineered the ETL landscape beyond a simple lift-and-shift approach, eliminating redundancies and modernizing only business-critical workloads.
• Applied business relevance analysis to identify and retire obsolete pipelines, reducing unnecessary migration effort and long-term operational costs.
• Established a modern Databricks architecture with governance capabilities through Unity Catalog, enabling a scalable and future-ready data platform.