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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.

Google BigQuery Cloud Migration Checklist
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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.

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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.

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1 Billion+

Lines of code translated automatically

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160+

Enterprise modernizations completed

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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:

Automated migration of IBM DataStage pipelines to PySpark on Google Cloud Dataproc
Conversion of DB2 tables to Google BigQuery for cloud-native analytics
Accelerated proof of concept validating migration accuracy and scalability
A proven framework for large-scale Google Cloud modernization initiatives
DOWNLOAD CASE STUDY
A Multinational Financial Services Company Migrates from IBM DataStage & DB2 to Google Cloud Platform

What Industry Analysts Say

Image (Rob Enderle)
Rob Enderle

ENDERLE GROUP

"Next Pathway is providing a solution that accelerates the migration process, giving organizations a faster route to realizing value from their data."
Eric Kavanagh
Eric Kavanagh

THE BLOOR GROUP

"SHIFT Cloud enables a remarkably swift and robust transition from traditional data warehousing to the cloud."

What a Google BigQuery migration covers

A successful migration to Google BigQuery requires a structured approach across planning, workload modernization, validation, and production deployment. This checklist helps teams reduce risk and accelerate time to value.

Migration Assessment and Planning

Assess existing data warehouses, ETL pipelines, reporting workloads, and dependencies before migration begins. Define migration scope, success criteria, timelines, and resource requirements to ensure a predictable migration journey.

Data and Workload Modernization

Migrate schemas, SQL code, ETL processes, and analytics workloads to Google BigQuery. Modernize legacy architectures to take advantage of BigQuery's scalable, cloud-native data platform while reducing operational complexity.

Testing, Validation, and Optimization

Validate migrated data, reports, dashboards, and business processes against source systems. Test for data accuracy, functional parity, performance, and cost efficiency to ensure workloads are production-ready before cutover.

Production Cutover and Adoption

Execute a controlled deployment plan that includes user acceptance testing, rollback procedures, governance controls, and operational readiness. Proper cutover planning minimizes business disruption and supports long-term success.

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