Leading Manufacturer Migrates from Informatica PowerCenter to Azure Data Factory and DBT
Leading Manufacturer Migrates from Informatica PowerCenter to Azure Data Factory and DBT
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What Next Pathway's Leading Manufacturer Case Study covers
A Leading Manufacturer migrated Informatica PowerCenter to Azure Data Factory and DBT as part of a broader Snowflake modernization initiative. Next Pathway’s automation platform delivered a 95% automated code conversion rate, accelerated legacy system modernization, and improved data processing performance.
Automated Code Conversion
Next Pathway used the SHIFT Product Platform to automate Informatica PowerCenter code conversion and unit testing for Azure Data Factory and DBT. The migration achieved a 95% automated conversion rate across complex workflows, mappings, and mapplets, reducing manual effort and accelerating delivery.
Enterprise Data Modernization
The migration positioned the manufacturer to modernize its Enterprise Data Warehouse on a scalable cloud based platform. By moving from legacy ETL systems to cloud native technologies, the company improved agility, scalability, and operational efficiency across its data environment.
Streamlined Cloud Native Architecture
The project consolidated multiple legacy systems into a modern cloud native environment built on Azure Data Factory, DBT, and Snowflake. This reduced future technical debt, supported the retirement of on premises systems, and enhanced overall data processing capabilities and performance.
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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