Google BigQuery Cloud Migration
Checklist
A proven, phase-by-phase framework for migrating your legacy EDW, Data Lake, and ETL pipelines to Google BigQuery faster and with full confidence.
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What Next Pathway's Google BigQuery Cloud Migration Checklist covers
A successful Google BigQuery Cloud migration requires a structured, phase by phase approach across discovery, code translation, validation, and cutover. Next Pathway's Google BigQuery Cloud Migration Checklist gives your team a complete roadmap, built on the same automation Next Pathway uses to deliver enterprise migrations end to end.
Planning and Onboarding
Define project roles, responsibilities, migration scope, and execution logistics before migration begins. Establish cloud environment setup, identify dependencies, and align business objectives across project teams early in the process. Early planning reduces risk, prevents bottlenecks, and creates a clear migration path.
Code Translation
Identify workloads, prioritize migration waves, and assess underlying code objects before translation starts. Define whether ETL pipelines will be migrated or modernized and ensure all required code is accounted for before execution. A structured translation strategy improves migration accuracy and execution efficiency.
Testing
Develop an end to end testing strategy at the start of the migration project. Involve business teams in defining test cases and success criteria while using automation to support CI/CD pipelines. Early and structured testing helps validate migrated workloads before cloud cutover and reduces operational risk.
1 Billion+
Lines of code translated automatically
160+
Enterprise modernizations completed
80%
Faster time-to-market for AI-ready infrastructure
Latest Google Cloud Migration Case Study
Next Pathway helped a multinational financial services company modernize its data platform by migrating IBM DataStage and DB2 to Google Cloud Platform. Using SHIFT, the company automated code translation to Google Cloud Dataproc and BigQuery, accelerating its cloud modernization journey and validating a scalable migration approach.
Read the case study to see how Next Pathway delivered:
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Discover how a multinational financial services company modernized IBM DataStage and DB2 on Google Cloud using SHIFT, accelerating migration validation and enabling scalable cloud-native analytics.
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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