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

Client Challenges and Requirements

• Point-to-point data integrations between on-premises source and target systems created data discrepancies and increased operational overhead.
• Legacy reporting architectures limited scalability and performance.
• Redundant ETL processes and duplicate data engineering efforts increased maintenance requirements and costs.
• High operational complexity made it difficult to manage distributed reporting workloads efficiently.
• Heavy dependence on on-premises systems constrained the evolution toward a cloud-native data architecture.
• Limited centralized visibility and governance made it challenging to manage data consistently across the ecosystem.
• Manual assessment and migration planning increased project timelines and execution risks.
• The organization was recommended 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 FulkrumAI to analyze workloads, evaluate the inventory, classify migration candidates, and establish data-driven modernization roadmaps.
• Leveraged AI-assisted code conversion to transform legacy Informatica workloads into Databricks PySpark using standardized migration patterns and reusable libraries.
• Re-engineered the ETL landscape beyond a lift-and-shift approach by identifying redundancies and modernizing business-critical workloads.
• Applied business relevance analysis to identify and retire obsolete pipelines, reducing unnecessary migration effort and long-term operational costs.
• Developed custom Databricks PySpark notebooks to make data from on-premises sources available within the Databricks environment.
• Processed and persisted data into Databricks Lakehouse tables to establish a scalable cloud-native data foundation.
• Migrated historical reporting data directly into the Silver layer, maintaining continuity of historical reporting and operational processes.
• Enabled data sharing across Databricks workspaces and non-Databricks environments using Delta Sharing.
• Established centralized governance through Unity Catalog, supporting a scalable and future-ready data platform.
• Developed custom Databricks PySpark notebooks, supported by FulkrumAI, to ingest data from multiple on-premises sources and process it into Databricks Lakehouse tables.
• Migrated target SQL Server tables and their historical data directly into the Databricks Silver layer, ensuring continuity of historical reporting and operational processes.

Tools & Technologies We Used