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Global Investment Bank Migrates from Oracle and DataStage to Snowflake

A strategic transformation from Oracle and DataStage to Snowflake, delivering improved performance, reduced operational complexity, and greater business agility.

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What Next Pathway's Global Investment Bank Case Study covers

A Global Investment Bank migrated Oracle and DataStage to Snowflake. Next Pathway's automation platform delivered 8 month end to end project delivery, 100% automated validation across all pipelines, and 3x faster query performance demonstrated in the proof of concept.

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Accelerated Migration Delivery

Next Pathway automated the migration of thousands of DataStage jobs and Oracle procedures to Snowflake using SHIFT Cloud technology. The project was completed on schedule with no business disruption, enabling the bank to modernize its data infrastructure while maintaining continuity for critical operations.

Automated Validation and Compliance

Automated testing and validation frameworks ensured every migrated workload performed as required before go live. Rigorous data validation across all stages supported data integrity, regulatory compliance, and reliable reporting workflows.

Faster Analytics and Reduced Complexity

The migration improved reporting and analytics speed, enabling more timely regulatory and client insights. By fully decommissioning legacy platforms, the bank reduced IT complexity and ongoing operational costs while creating a scalable cloud data foundation for future analytics and AI initiatives.

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1 Billion+

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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.

Latest Insights on Modernization

Cloud Migration Resources

Azure Synapse and ADF to Snowflake
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