Prominent US hospital migrates from Netezza and IBM DataStage to
Google Cloud Platform
Prominent US hospital migrates from Netezza and IBM DataStage to Google Cloud Platform
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What Next Pathway's Prominent US Hospital Case Study covers
A Prominent US Hospital migrated Netezza and IBM DataStage to Google Cloud Platform in just 6 months. Next Pathway’s automation platform translated more than 50,000 Netezza objects and SQLs, migrated over 4,000 DataStage jobs, and enabled operational efficiencies and cost savings through legacy system decommissioning.
Large Scale Legacy Data Warehouse Migration
Next Pathway planned, translated, and migrated the hospital’s legacy Netezza and IBM DataStage environment to a cloud native Google Cloud Platform architecture. The migration included Google BigQuery, Dataproc, and associated BI reporting systems to modernize enterprise data operations.
Automated Translation at Enterprise Scale
Using SHIFT automated code translation, Next Pathway successfully migrated more than 50,000 Netezza objects and SQLs along with over 4,000 DataStage jobs within 6 months. The automation driven approach accelerated large scale migration execution while reducing complexity and manual effort.
Lift and Modernize Cloud Transformation
The project followed a lift and modernize strategy instead of a traditional lift and shift migration. The modernization introduced new cloud native technologies, improved governance processes, reengineered data models, and enabled the hospital to achieve operational efficiencies and cost savings by retiring legacy systems and third party services.
1 Billion+
Lines of code translated automatically
160+
Enterprise modernizations completed
80%
Faster time-to-market for AI-ready infrastructure
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What Industry Analysts Say
Rob Enderle
ENDERLE GROUP
Eric Kavanagh
THE BLOOR GROUP
What a structured Snowflake migration covers
A successful migration to Snowflake requires a clear plan across four disciplines.
Discovery and dependency mapping
Every code object, ETL pipeline, and data dependency needs to be identified and documented before migration begins. Hidden complexity discovered mid-migration creates delays and risk.
Code translation and modernization
Legacy SQL, stored procedures, and ETL pipelines must be converted into optimized, Snowflake-native workloads. Automated translation eliminates manual rework and ensures full coverage.
Validation and functional parity
Every migrated workload must be validated against the legacy source to confirm data accuracy and functional equivalence before cutover. 100% parity is not optional.
Production cutover planning
Cutover requires a defined plan covering resource allocation, rollback decisions, and business sign-off. Teams that plan cutover from the start move faster and with greater confidence.
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