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Hadoop to Azure Databricks and Azure Data Factory

Multinational Insurance provider migrates from Hadoop to Azure Databricks and Azure Data Factory

Multinational Insurance provider migrates from Hadoop to Azure Databricks and Azure Data Factory
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What Next Pathway's Multinational Insurance Provider Case Study covers

A Multinational Insurance Provider migrated Hadoop to Azure Databricks and Azure Data Factory as part of its cloud modernization strategy. Next Pathway’s automation platform translated legacy HQL, Shell, and Python code to Databricks, modernized on premises Hadoop pipelines for Azure, and implemented reusable Databricks connectivity frameworks for future migrations.

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Hadoop to Azure Modernization

The company engaged Next Pathway to migrate legacy data pipelines and applications running on an on premises Hadoop ecosystem to Azure. Existing Apache Hive and CA scheduling based pipelines were modernized for Azure Databricks and Azure Data Factory to support cloud based data operations.

Automated Legacy Code Translation

Using SHIFT Cloud, Next Pathway translated HQL workloads to Databricks Spark SQL along with Shell and Python scripts to Databricks compatible frameworks. The migration approach accelerated conversion of legacy workloads while preserving orchestration logic and application dependencies.

Reusable Cloud Integration Framework

Next Pathway implemented connectivity between PowerShell and Databricks through the Databricks CLI to streamline cloud integration and orchestration. The resulting framework established a reusable migration template for future applications across the organization.

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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 Snowflake Case Study

Next Pathway helped a UK-based asset management leader migrate from Azure Synapse and Azure Data Factory to Snowflake with zero business disruption and 100% automated code translation.

Read the case study to see how Next Pathway delivered:

End-to-end migration completed in just 10 months
100% automated code translation with SHIFT
Seamless compliance and financial reporting throughout cutover
Real-time analytics and AI-ready data on a unified platform
Download Case study
Snowflake Modernization Guide

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 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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