The Challenge
A large enterprise was running a massive on-premises Hadoop environment that had grown unwieldy over the years. They faced significant operational and cost challenges:
- Over 1,000 servers requiring constant maintenance and upgrades
- Complex Oozie job scheduling with limited visibility
- High infrastructure costs with underutilized resources
- Difficulty scaling for peak workloads
- Legacy code in multiple languages (Java, Python, Hive, Spark)
Our Approach
CloudBrainy designed and executed a phased migration strategy to minimize risk and ensure business continuity:
- Comprehensive workload assessment and dependency mapping
- Terraform-based infrastructure as code for GCP environments
- Cloud Composer implementation to replace Oozie scheduling
- Ephemeral Dataproc clusters for cost-efficient processing
- Code refactoring and optimization for cloud-native performance
- Parallel run validation before cutover
Key Deliverables
Terraform Infrastructure
Multi-environment deployment with networks, firewalls, IAM, and monitoring
Cloud Composer Orchestration
Modern Airflow-based workflow replacing legacy Oozie jobs
Ephemeral Dataproc Clusters
Dynamic clusters provisioned by Airflow DAGs for optimal cost
Code Refactoring
20,000+ lines of Python, Spark, and Hive optimized for GCP
Results & Impact
80%
Cost Reduction
1,000+
Servers Migrated
20,000+
Lines of Code Refactored
Zero
Business Disruption