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Multinational Insurance and Financial Services Provider Migrates Legacy Systems to Databricks

Multinational Insurance and Financial Services Provider Migrates Legacy Systems to Databricks

Multinational Insurance and Financial Services Provider migrates legacy systems to Databricks
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What Next Pathway's Multinational Insurance and Financial Services Provider Case Study covers

A Multinational Insurance and Financial Services Provider migrated legacy systems to Databricks in just 3 months. Next Pathway’s automation platform translated 20+ extract applications, delivered significant cost savings, and enabled drastic execution time reduction for large applications.

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Accelerated Databricks Migration

Next Pathway migrated legacy extract pipelines from Apache Hive, Oozie, and CA Technologies to Azure native environments using Azure Databricks and Delta Lake. Using SHIFT Cloud automation, the team translated more than 20+ extract applications within just 3 months, accelerating modernization timelines.

Cost Optimization and Performance Gains

The migration replaced a single large Hadoop cluster with multiple right sized clusters grouped by application requirements. This optimization reduced infrastructure costs, improved resource consumption efficiency, and enabled drastic execution time reduction through Delta Lake consistency and enhanced ACID transactions.

Simplified Data Operations

The new cloud architecture streamlined extract generation and reporting by enabling faster processing and improved SLA performance. A common extract template also simplified future business extract requirements while consolidating multiple technology stacks into a single target platform for lower support and maintenance effort.

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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 Case Study

Next Pathway helped a leading US health insurance provider modernize its legacy data platform by migrating from IBM DB2 and DataStage to Azure SQL, Azure Data Factory, and Databricks—accelerating analytics, improving operational efficiency, and enabling AI readiness.

Read the case study to see how Next Pathway delivered:

Modernized legacy IBM DB2 and DataStage workloads to Azure and Databricks
Automated migration of databases, ETL pipelines, and scheduling frameworks
Improved scalability, operational efficiency, and data accessibility
Built a cloud-native, AI-ready platform for advanced analytics and future innovation
Download Case study
Modernizing a US Health Insurance Provider’s Data Platform From DB2 & DataStage to Azure & 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 successful Databricks migration covers

A successful migration to Databricks requires a structured approach across four critical phases to modernize legacy data and analytics platforms.

Legacy system discovery and assessment

Identify legacy databases, ETL workflows, reporting tools, and application dependencies before migration begins. A comprehensive assessment minimizes risk, uncovers hidden complexities, and enables accurate migration planning.

Code conversion and workload modernization

Legacy SQL, ETL jobs, stored procedures, and analytics workloads are transformed into Databricks-native pipelines using Apache Spark and Delta Lake. Automated code conversion accelerates migration while reducing manual effort.

Data validation and performance optimization

Validate migrated data, pipelines, and business logic to ensure accuracy, consistency, and functional equivalence. Optimize workloads for scalability, performance, and cost efficiency before production deployment.

Production deployment and operational readiness

Execute a well-defined cutover strategy with rollback planning, governance, user validation, and production monitoring. A structured deployment ensures a seamless transition with minimal business disruption.

Latest Insights on Modernization

Cloud Migration Resources

Databricks Data Migration Checklist
Databricks Data Migration Checklist

A step-by-step checklist for enterprises to ensure a smooth migration to Databricks.

DOWNLOAD CHECKLIST

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DB2 & DataStage to Azure & Databricks
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