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Azure Data Factory Cloud Migration
Checklist

A comprehensive checklist for planning, migrating, validating, and optimizing ETL and data workloads on Azure Data Factory.

Azure Data Factory Cloud Migration Checklist
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What Next Pathway's Azure Data Factory Cloud Migration Checklist covers

A successful Azure Data Factory Cloud migration requires a structured, phase-by-phase approach across discovery, code translation, validation, and cutover. Next Pathway's Azure Data Factory 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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Assessment & Discovery

Discover legacy ETL pipelines, dependencies, data lineage, and orchestrations across your environment. The checklist helps teams identify data flows, linked services, datasets, and control flows while uncovering compatibility issues and migration risks early in the process.

ETL Migration and Translation

Translate legacy ETL pipelines into Azure Data Factory pipelines, activities, and triggers with a structured migration approach. The checklist also guides teams through handling unsupported functions, prioritizing workloads, and validating translated code before deployment.

Acceptance Testing and Validation

Build a comprehensive testing strategy to validate migrated ETL pipelines before cloud cutover. The checklist emphasizes automated test generation, rapid issue remediation, and the use of Next Pathway’s automation tools to streamline migration assurance.

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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 Cloud Migration Case Study

Next Pathway helped a global financial services company migrate Hadoop workloads to Azure Databricks and Azure Data Factory, accelerating cloud modernization through automated code translation and reusable integration frameworks.

Read the case study to see how Next Pathway delivered:

End-to-end Hadoop modernization for scalable cloud-native operations
100% automated HQL, Shell, and Python code translation with SHIFT
Faster cloud migration with minimal disruption to business processes
Reusable Databricks integration model created to accelerate future projects
DOWNLOAD CASE STUDY
Global Financial Services Company Migrates from Hadoop to Azure Databricks and Azure Data Factory

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 Structured Azure Data Factory Migration Covers

A successful migration to Azure Data Factory requires a clear strategy across four critical areas.

Discovery and dependency mapping

Every ETL workflow, data source, integration point, and dependency must be identified and assessed before migration begins. Early discovery helps uncover hidden complexities and reduces project risk.

Code translation and modernization

Legacy ETL processes, workflows, and orchestration logic must be transformed into Azure Data Factory pipelines. Automated conversion accelerates migration, reduces manual effort, and improves consistency.

Validation and functional parity

Migrated pipelines must be thoroughly tested to ensure data accuracy, workflow reliability, and functional equivalence. Validation is essential for maintaining business continuity and operational confidence.

Production cutover planning

A successful cutover requires detailed planning around deployment schedules, resource allocation, rollback procedures, and stakeholder sign-off. A structured approach minimizes disruption and accelerates time to value.

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