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DataStage & DB2 to Google Cloud Platform

A Multinational Financial Services Company Migrates from IBM DataStage & DB2 to Google Cloud Platform

A Multinational Financial Services Company Migrates from IBM DataStage & DB2 to Google Cloud Platform
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What Next Pathway's A Multinational Financial Services Company Case Study covers

A Multinational Financial Services Company migrated IBM DataStage and DB2 to Google Cloud Platform as part of its enterprise cloud modernization initiative. Next Pathway’s automation platform translated DataStage pipelines to PySpark on Google Cloud Dataproc, converted DB2 tables to Google BigQuery, and demonstrated accelerated migration feasibility for larger scale cloud transformation programs.

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Automated DataStage and DB2 Migration

Next Pathway used its SHIFT Translator to convert IBM DataStage ETL pipelines to PySpark running on Google Cloud Dataproc. The migration also translated DB2 tables to Google BigQuery, enabling modernization of the company’s on premises data warehouse environment.

Accelerated Cloud Migration Validation

The project began with a proof of concept designed to validate the accuracy and efficiency of automated code translation. This approach demonstrated the acceleration benefits and technical feasibility required to support a larger scale enterprise migration initiative.

Scalable Google Cloud Modernization

The migration established a proven framework for efficiently modernizing IBM DataStage pipelines and DB2 workloads on Google Cloud Platform. The resulting architecture enabled scalable cloud native data processing capabilities using Google BigQuery and Dataproc.

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1 Billion+

Lines of code translated automatically

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160+

Enterprise modernizations completed

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80%

Faster time-to-market for AI-ready infrastructure

Latest Snowflake Case Study

Next Pathway helped a UK-based asset management leader migrate from Azure Synapse and Azure Data Factory to Snowflake with zero business disruption and 100% automated code translation.

Read the case study to see how Next Pathway delivered:

End-to-end migration completed in just 10 months
100% automated code translation with SHIFT
Seamless compliance and financial reporting throughout cutover
Real-time analytics and AI-ready data on a unified platform
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Snowflake Modernization Guide

What Industry Analysts Say

Image (Rob Enderle)
Rob Enderle

ENDERLE GROUP

"Next Pathway is providing a solution that accelerates the migration process, giving organizations a faster route to realizing value from their data."
Eric Kavanagh
Eric Kavanagh

THE BLOOR GROUP

"SHIFT Cloud enables a remarkably swift and robust transition from traditional data warehousing to the cloud."

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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