A World Leader in Aircraft Engines migrates from Greenplum to Amazon Redshift
A World Leader in Aircraft Engines migrates from Greenplum to Amazon Redshift
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What Next Pathway's A World Leader in Aircraft Engines Case Study covers
A World Leader in Aircraft Engines migrated Greenplum to Amazon Redshift in just 6 weeks. Next Pathway’s automation platform translated 500 complex stored procedures and more than 70 views, achieved 72% faster migration acceleration over manual approaches, and enabled automated high quality code conversion with rigorous QA testing.
Accelerated Greenplum to Redshift Migration
Next Pathway supported the migration of a large Greenplum data warehouse environment to Amazon Redshift for a global aircraft engines and avionics company. The initial engagement focused on translating 500 highly complex stored procedures to demonstrate migration speed and feasibility.
Automated Translation and Quality Assurance
Within just 6 weeks, Next Pathway translated all 500 stored procedures and more than 70 views using its automated migration platform. All translated code was validated through a rigorous 4 point QA unit testing process to ensure complete and high quality conversion accuracy.
Proven Migration Acceleration
The results demonstrated a 72% acceleration compared to manual migration approaches, validating the effectiveness of Next Pathway’s automated migration methodology. Following the successful pilot, the company awarded Next Pathway the remaining Greenplum code base migration to Amazon Redshift.
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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