Legacy Platform Migration
Tier-1 Railroad
Scale of delivery
8000+
Data entities
4000+
Data pipelines
5 PB+
Of data to be moved
We inventory every SQL workload and ETL dependency before scoping begins. Nothing is estimated blind.
Our discovery phase maps lineage explicitly. Pipelines are sequenced for migration based on dependency chains, not convenience.
We architect for the target state from day one. Databricks is not a destination for your old pipelines. It is a new platform designed around how your data needs to move and be consumed.
We do not hand off a migration. We hand off a production environment. AutoDQ is embedded into delivery to validate quality at every stage, not just at cutover.
Most Netezza migrations stall at scoping. We offer a focused discovery engagement to inventory your environment, identify risk, and define a migration path before any large commitment is made.
Book a Scoping ConversationA structured, phased framework built from real delivery experience.
This approach reflects best practices from real migration delivery playbooks.
what we did
A major North American railroad was operating a large-scale Netezza 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
Limited ability to support advanced analytics and AI initiatives
If your team is managing a Netezza environment today, you have likely already felt at least one of these. Most organizations we speak with are feeling all four.
The organization needed to modernize without disrupting mission-critical systems.
KData led the migration to Databricks, starting with a full discovery of data assets, pipelines, and dependencies.
Assessed and prioritized hundreds of tables, SQL workloads, and ETL jobs
Translated and re-engineered Netezza SQL and pipelines into Databricks
Designed and implemented a lakehouse architecture (bronze, silver, gold)
Established data governance and access control using Unity Catalog
Validation and Cutover
Data reconciliation and testing powered by our AutoDQ accelerator, which automates quality checks across thousands of pipelines instead of relying on manual spot-checks
SLA validation with documented pass/fail criteria before any cutover decision is made
Parallel run and controlled cutover with i-QA providing continuous quality assurance throughout
Optimization
Ongoing data quality monitoring using AutoDQ, giving your team visibility into pipeline health without building tooling from scratch
The result was not just a migration, but a production-ready Databricks platform.
Reduced average pipeline runtime by over 60%, eliminating the batch job bottlenecks that were impacting daily operations
Cut platform infrastructure and licensing costs significantly, with the new Databricks environment consolidating what previously required multiple systems
Delivered a production-ready lakehouse in phases, with zero disruption to mission-critical railroad operations throughout the transition
The client's data team moved from maintaining legacy ETL to building new analytics and AI use cases within weeks of cutover
8000+
Data Entities Migrated
4000+
Data Pipelines Built
5 PB+
Data Moved to Databricks
The transition was executed without disrupting core business operations.