When migrating from an on-premises Hadoop cluster to Amazon EMR on EC2, it is crucial to ask the right questions to ensure a smooth transition. Here’s a comprehensive checklist of critical considerations:

1. Current On-Premises Setup

  • Distribution: What is the current Hadoop distribution (Cloudera, Hortonworks, Apache Hadoop) and version?
  • Infrastructure:
    • Number of nodes in the cluster
    • Node configurations (CPU, RAM, Storage)
  • Services: Which components are in use? (HDFS, YARN, Hive, HBase, Spark)
  • Customizations: Any custom configurations or optimizations?

2. Data Migration

  • What is the total data size to be migrated from on-prem to EMR?

  • Is the data static or continuously generated (streaming data)?

Are there any sensitive data that require encryption during transit and at rest?

  • What is the most suitable data transfer method (AWS Direct Connect, AWS DataSync, S3 Transfer Acceleration, Snowball, etc.)?

  • Are there any data partitioning and compression strategies that need to be applied during migration?

3. Security and Access Control

  • How is user authentication and authorization managed on the on-prem cluster (Kerberos, LDAP, Ranger, Sentry)?

  • What are the existing IAM roles and policies for accessing Hadoop services?

  • How will access control be managed in EMR (IAM roles, security groups, AWS KMS for encryption)?

  • Are there any existing network security configurations (VPC, Subnets, Security Groups) needed for EMR?

4. Network Configuration

  • Should EMR be launched in a VPC with public or private subnets?

  • What is the preferred network configuration (NAT Gateway, VPC Peering, Direct Connect)?

  • Are there any network performance requirements (latency, bandwidth)?

5. Storage Management

  • Where will the data be stored in AWS (S3, EBS, or HDFS on EMR)?

  • What are the retention policies for S3 data (lifecycle policies, versioning, intelligent tiering)?

  • Are there any requirements for data backup and disaster recovery?

6. Cluster Configuration

  • What will be the EMR cluster type (transient, long-running, or serverless)?

  • What instance types and sizes should be used for master, core, and task nodes?

  • Should EMR be configured with Auto Scaling?

  • Are there any custom AMIs or bootstrap actions required?

7. Application and Workload Migration

  • What are the existing applications running on Hadoop (Spark, Hive, HBase, Pig, Flink)?

  • Are there any custom scripts, UDFs, or libraries that need to be migrated?

  • Are the applications compatible with the EMR version being considered?

  • Are there any SLAs or performance benchmarks that must be met on EMR?

8. Cost Optimization

  • What are the expected EMR costs, including EC2, S3, and data transfer?

  • Are Spot Instances suitable for any part of the workload?

  • Can Reserved Instances be used for predictable workloads?

  • Are there any cost optimization tools (AWS Cost Explorer, AWS Budgets) in use?

9. Monitoring and Troubleshooting

  • How will the EMR cluster be monitored (CloudWatch, CloudTrail, EMR Metrics)?

  • What are the logging configurations for EMR (CloudWatch Logs, S3 logging)?

  • How will alerts be configured for critical failures?

10. Post-Migration Validation and Testing

  • Acceptance Criteria:
    • Data integrity validation
    • Performance benchmarks
    • Security compliance checks
  • Testing Strategy:
    • Job migration validation
    • Application functionality testing
    • Performance testing
  • Rollback Plan:
    • Fallback procedures
    • Data recovery strategy
    • Service continuity plan

Pro Tip: Start with a small proof-of-concept migration before attempting the full production workload migration.

Additional Resources