case study

Driving Application Migration from Cloudera to Google Cloud

Technology

Business Impacts

Savings on infrastructure, storage, and migration costs

~30+

VMs Migrated

52

enterprise-wide applications migrated to Google Cloud

~40%

migration cells running independently

Customer Key Facts

  • Country : US Central
  • Industry : Technology

Problem Context

The client is a multinational professional services company that specializes in IT services and consulting and a leading SI with global offices. The client’s professional services include digital, cloud, and operational capabilities spread across 70 locations worldwide.

Since most of the client’s applications were running on an on-premise legacy platform, the client sought to migrate from Cloudera to Google Cloud Platform after the expiry of their contract with Cloudera.

Challenges

  • High maintenance and hardware cost and complex scalability procedures
  • Performance and optimization issues in the current CAP(1.0) use cases
  • Pipeline and environment unstability
  • Limiting ability to meet the needs of growing data

Technologies Used

BigQuery

BigQuery

Google Cloud Storage

Google Cloud Storage

Dataproc

Dataproc

Cloud Function

Cloud Function

Cloud Composer

Cloud Composer

Pub/Sub

Pub/Sub

Cloud Scheduler

Cloud Scheduler

Seamless migration from Cloudera to Google Cloud

Solution

  • Quantiphi assisted the client with migration efforts for their internal applications from CAP 1.0 (Hadoop) to Google Cloud environment
  • Quantiphi provided an end-to-end solution design of the new architecture by leveraging Google Cloud services

Results

  • Implemented Google Cloud to align with the client’s multi-cloud strategy
  • Migrated 9 applications from Cloudera to Google Cloud
  • Identified and implemented best practices for platform compatibility, optimization opportunities
  • Migrated all features to Google Cloud despite functionality limitations
  • Achieved higher accuracy on Google Cloud as compared to on-prem
  • Facilitated seamless coordination between source and application teams for source data load issues

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