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Modernizing a Leading European Multinational Bank’s Data Platform: From Cloudera & Hadoop to Google Cloud Platform

A successful enterprise modernization that transformed Oracle and Informatica into a modern Snowflake ecosystem. 

Modernizing a Leading European Multinational Bank’s Data Platform- From Cloudera & Hadoop to
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What Next Pathway’s European Multinational Bank Case Study Covers

A leading European multinational bank modernized its Cloudera and Hadoop platform to Google Cloud Platform with Next Pathway’s Enterprise SLM Platform, creating a scalable, compliant foundation for data processing and advanced analytics.

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Cloudera & Hadoop to GCP Modernization

A leading European multinational bank modernized its legacy Hadoop-based platform, migrating HDFS storage to Google Cloud Storage, Spark workloads to Dataproc, and 1,000+ Iceberg, Oracle, and Cassandra tables to BigQuery in approximately six months.

Metadata-Driven Ingestion & Code Modernization

Next Pathway replaced table-specific ingestion with a generic, metadata-driven framework, consolidated hundreds of Airflow DAGs into 3–4 reusable Cloud Composer DAGs, and converted 300+ Scala and 350+ Java files while preserving SCD Type 2 and schema evolution logic.

Scalable Foundation for Advanced Analytics

The modernized platform, aligned with the bank’s GCP reference architecture and deployed through Terraform-based automation, supports 32 consumption applications across business units while meeting strict regulatory, security, and audit requirements.

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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

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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 Google Cloud migration covers

A successful migration to Google Cloud Platform requires a clear plan across four disciplines.

Discovery and dependency mapping

Every Scala and Java application, ingestion process, and orchestration dependency needs to be identified before migration begins. Mapping dependencies across ingestion, applications, and control processes reduces delays and risk.

Code conversion and modernization

Legacy Scala, Java, and Spark code must be converted into modular, GCP-aligned workloads on Dataproc and BigQuery. Automated conversion preserves business logic, including SCD Type 2 processing and schema evolution.

Validation and functional parity

Table-specific ingestion and hundreds of Airflow DAGs should be consolidated into a generic, metadata-driven framework. A few reusable Cloud Composer DAGs simplify monitoring, maintenance, and onboarding of new data sources.

Production cutover planning

Every migrated component must pass unit testing and SIT against the legacy platform before go-live. Terraform-based deployment keeps environments consistent and meets strict security, audit, and compliance requirements.

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