case study

Oracle Exadata To Cloud Migration


Business Impacts


Of data migrated to Google Cloud

Faster access to real-time querying

Improved actionable insights

Customer Key Facts

  • Location : North America
  • Industry : Insurance

Problem Context

The customer, a leading P&C insurer company, had an on-prem enterprise data warehouse (EDW) hosted on Oracle Exadata which had evolved into a massive and complex system that was difficult to own and maintain. This made the native connection and visualization of data sets from the EDW even more difficult. The customer wanted to migrate to Google Cloud Platform (GCP) to facilitate flexible consumption of their EDW data.



  • Need for more efficient methods to build and manage ETL/ELT Extract Transform Load processes to transfer the data between systems for real-time analysis
  • Lack of an automated system to build, test, and deploy software for continuous integration and testing between systems

Technologies Used

Google's BigQuery

Google's BigQuery

Cloud Data Fusion

Cloud Data Fusion

Cloud Storage

Cloud Storage

Cloud Composer

Cloud Composer

Cloud Dataproc

Cloud Dataproc

Building A Processing Pipeline To Ingest, Process And Load Raw Data From Claims System Onto GCP


Quantiphi migrated 150 terabytes of Claims, Premiums, Policies, and Financial transactions data from Oracle Exadata to Google’s BigQuery and built a financial transaction model, a production-ready claim center data warehouse on Google Cloud, and an enterprise-grade DLP solution to manage its RED data.

Quantiphi also created a scalable data architecture and data processing pipelines that will ingest, process and load the raw data from the Claims system onto GCP. 


  • Petabyte-scale functionalities, and flexible pricing
  • Improved data governance and security
  • GCP’s scalable architecture reduced querying time, automated backups, and decreased downtime 
  • Significant cost savings for the customer
  • Higher performance with BigQuery over the on-prem Oracle database 
  • Flexible data ingestion pipelines and serverless capabilities enabled data warehouse creation for analytics and machine learning
  • Improved actionable insights through faster and flexible consumption of EDW data on Google Cloud 
  • Real-time analytics

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