A public services and procurement provider migrates from DataStage and Netezza to Azure Synapse
A public services and procurement provider migrates from DataStage and Netezza to Azure Synapse
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What Next Pathway's A Public Services and Procurement Provider Case Study covers
A public services and procurement provider migrated DataStage and Netezza to Azure Synapse using Next Pathway’s automated migration platform. The project delivered migration of approximately 400 DataStage jobs, optimized long running batch execution processes, and modernized legacy ETL architecture into a cloud native ELT framework.
Automated DataStage and Netezza Migration
Next Pathway used its Analyzer tool to identify code dependencies and organize the migration into multiple execution sprints. Using SHIFT Cloud, the team converted DataStage Parallel and Sequence Jobs along with embedded Netezza SQL queries into Azure Synapse pipelines.
Cloud Native ELT Modernization
The migration transformed the company’s legacy ETL architecture into a modern ELT framework running on Azure Synapse. This enabled data transformations to execute directly on the Azure database engine, improving scalability and supporting next generation cloud native requirements.
Optimized Batch Processing Performance
Next Pathway optimized long running SCD Type 2 DataStage jobs and delivered optimized Synapse pipelines for end to end batch processing. The project also included migration of C based parameter initialization code to Python, resulting in a fully integrated Azure cloud solution with substantial reductions in batch execution times.
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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