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Modernizing a US Health Insurance Provider’s Data Platform: From DB2 & DataStage to Azure & Databricks

US Health Insurance Provider Modernizes Legacy Data Platform to Accelerate Analytics, Improve Operational Efficiency, and Enable AI Readiness 

Modernizing a US Health Insurance Provider’s Data Platform From DB2 & DataStage to Azure & Databricks
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What Next Pathway’s US Health Insurance Provider Case Study Covers

 A leading US health insurance provider modernized its legacy data ecosystem by migrating from IBM DB2 and DataStage to Azure SQL, Azure Data Factory, and Databricks. Using Next Pathway’s automation platform, the organization accelerated modernization, improved scalability, and established a cloud-native foundation for advanced analytics and AI. 

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Legacy Platform Modernization

Next Pathway transformed a complex environment spanning legacy databases, ETL processes, scripting frameworks, and scheduling tools into a modern Azure-based architecture, reducing complexity and improving operational efficiency. 

Automated Migration at Scale

Through automation and engineering-led modernization, the team migrated legacy workloads, streamlined data pipelines, and accelerated delivery while maintaining data integrity and minimizing business disruption. 

Foundation for Analytics and AI

The modern cloud platform enhanced data accessibility, improved analytics performance, and created a scalable foundation to support future AI, reporting, and digital transformation initiatives. 

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1 Billion+

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160+

Enterprise modernizations completed

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80%

Faster time-to-market for AI-ready infrastructure

Latest Databricks Migration Case Study

A multinational insurance and financial services provider accelerated legacy application modernization with Databricks and Delta Lake.

Read the case study to see how Next Pathway delivered:

20+ extract applications migrated in 3 months
Hadoop, Hive, and Oozie workloads modernized
Improved performance and operational efficiency
Lower infrastructure and maintenance costs
Download Case study
Multinational Insurance and Financial Services Provider migrates legacy systems to Databricks

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 Databricks Migration Covers

A successful migration to Databricks requires a comprehensive approach spanning discovery, modernization, validation, and deployment.

Discovery and dependency mapping

Every data pipeline, workflow, script, and dependency must be identified and assessed before migration begins. Early discovery helps reduce risk, uncover hidden complexity, and establish a clear modernization roadmap.

Code translation and modernization

Legacy SQL, ETL processes, stored procedures, and batch workloads must be transformed into Databricks-native architectures. Automated translation accelerates migration while reducing manual effort and improving consistency.

Validation and functional parity

Migrated workloads must be thoroughly validated to ensure data accuracy, business logic integrity, and functional equivalence. Comprehensive testing minimizes risk and ensures confidence in production readiness.

Production cutover planning

A successful cutover requires detailed planning, including workload scheduling, environment readiness, rollback procedures, and stakeholder sign-off. Proper execution ensures a smooth transition with minimal business disruption.

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