Legacy Platform Migration
Tier-1 Railroad
Use Case
A major North American railroad was operating a large-scale DB2 environment supporting critical reporting, operations, and planning workflows.
Over time, the platform became a bottleneck.
Long-running batch jobs impacting daily operations
High infrastructure and licensing costs
Complex, tightly coupled ETL pipelines and database logic
Limited ability to support advanced analytics and AI initiatives
The organization needed to modernize without disrupting mission-critical systems.
KData led the migration to Databricks, starting with a full discovery of data assets, schemas, pipelines, SQL workloads, and dependencies.
Assessed and prioritized schemas, tables, SQL workloads, stored logic, and ETL jobs
Migrated schema and data into a Databricks lakehouse architecture using bronze, silver, and gold layers
Refactored DB2 SQL, ingestion logic, and transformation pipelines for Databricks
Implemented governance, access control, and auditability using Unity Catalog
Executed a phased migration with validation, parallel runs, and controlled cutover
The result was not just a migration, but a production-ready Databricks platform.
Improved pipeline performance and reliability
Reduced platform complexity and operational overhead
Enabled a unified foundation for analytics, reporting, and AI
The transition was executed without disrupting core business operations.
A structured, phased framework built from real delivery experience.
This approach reflects best practices from real migration delivery playbooks.
Treating the migration as a database copy instead of a platform redesign
Underestimating schema, SQL, stored logic, and ETL complexity
Migrating obsolete logic and low-value workloads without reassessment
Ignoring downstream dependencies, security mapping, and validation
Carrying DB2-specific patterns directly into Databricks without refactoring
We address these directly with a production-first approach.