Modernizing a Global Luxury Brand’s Data Platform: From IBM DB2 & SQL Server to Snowflake & DBT
Building a unified Snowflake data platform from legacy IBM DB2, SQL Server, CDM, and Talend environments.
Next Pathway is an Elite Snowflake Partner, trusted by global enterprises to deliver end-to-end modernization, from deep discovery to production cutover.
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What Next Pathway's Global Luxury Brand Case Study Covers
A Global Luxury Brand modernized IBM DB2, SQL Server, Cognos Data Manager, and Talend to Snowflake and DBT in 12 months. Next Pathway’s automation platform translated 6,000+ legacy objects, converted CDM and Talend ETL workloads to DBT, and delivered a scalable cloud native data platform built for advanced analytics and AI.
Legacy Platform Modernization
The customer needed to modernize a fragmented environment spanning IBM DB2, SQL Server, Cognos Data Manager, Talend, and custom SQL scripts. Next Pathway translated more than 6,000 legacy objects and migrated databases and ETL workloads to Snowflake and DBT within a 12 month transformation program.
Automated ETL and SQL Conversion
Next Pathway converted Cognos Data Manager ETL jobs to DBT and Linux shell scripts while repointing Talend workloads to Snowflake and translating them to DBT. Proprietary SQL incompatibilities were resolved through automated translation and targeted remediation to ensure Snowflake compatibility.
Cloud Native Foundation for Analytics and AI
The modernization delivered a unified Snowflake based data platform with modular DBT transformations and scalable data access across business units. The new architecture improved analytics performance, reduced dependency on legacy technologies, and established a foundation for future AI, machine learning, and real time analytics initiatives.
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
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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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