IBM Cloud Migration
Checklist
A proven, phase-by-phase framework for migrating your legacy EDW, Data Lake, and ETL pipelines to IBM Cloud faster and with full confidence.
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What Next Pathway's IBM Cloud Migration Checklist covers
A successful IBM Cloud migration requires a structured, phase by phase approach across discovery, code translation, validation, and cutover. Next Pathway's IBM Cloud Migration Checklist gives your team a complete roadmap, built on the same automation Next Pathway uses to deliver enterprise migrations end to end.
Project Planning and Onboarding
Build a clear migration framework before execution begins. Define project scope, roles and responsibilities, cloud environment setup, and the logistics required to support delivery. Align business objectives, success criteria, risks, and dependencies early to avoid delays later in the migration process.
Code Translation
Identify which workloads will be migrated, determine migration priorities, and map the underlying code objects for each migration wave. Establish a clear approach for ETL pipeline migration or modernization and ensure all required code is accounted for before translation begins.
Testing
Create an end to end testing strategy at the start of the project to prioritize workloads and validate migration success. Involve business teams in test case development, clearly define success criteria, and use automation to accelerate CI/CD execution and workload validation before cloud cutover.
1 Billion+
Lines of code translated automatically
160+
Enterprise modernizations completed
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.
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Free Step-by-Step Guide
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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.
Latest Insights on Modernization
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Cloud Migration Resources
Snowflake Data Migration Checklist
A step-by-step checklist for enterprises to ensure a smooth migration to Snowflake.
Databricks Data Migration Checklist
A step-by-step checklist for enterprises to ensure a smooth migration to Databricks.
