CIO Strategy for Migrating Oracle (on-premise) to Aurora PostgreSQL-AWS

I frequently collaborate with the CIO office to devise strategies and design cloud-based solutions. Here are some insights for migrating Oracle on-premise to Amazon Aurora PostgreSQL

I. Executive Summary

Migrating from Oracle to Amazon Aurora PostgreSQL can significantly reduce licensing costs, improve scalability, and leverage AWS's advanced features. This strategy outlines a comprehensive approach to ensure a successful migration using AWS Database Migration Service (DMS), AWS Schema Conversion Tool (SCT), and robust validation processes.

II. Goals and Objectives

1. Cost Reduction: Minimize Oracle licensing and support costs.

2. Scalability: Leverage Aurora’s scalability and high availability.

3. Performance Improvement: Optimize database performance and maintenance.

4. Risk Mitigation: Ensure data integrity and minimize downtime during migration.

5. Compliance: Maintain compliance with industry regulations and standards.

III. Strategy Overview

1. Assessment and Planning

- Project Team Formation: Assemble a cross-functional team including DBAs, developers, system administrators, and AWS specialists.

- Current State Analysis: Inventory all Oracle databases, applications, and dependencies.

- Migration Planning: Develop a detailed migration plan including timelines, resource allocation, and risk management.

2. Schema Conversion

- Use AWS SCT: Convert Oracle schema to PostgreSQL-compatible schema.

- Identify and resolve any compatibility issues.

- Convert database objects such as tables, indexes, and stored procedures.

3. Data Migration

- Use AWS DMS: Configure AWS DMS for data replication.

- Set up source and target endpoints.

- Create and configure replication tasks.

- Perform initial data load and enable ongoing replication to ensure minimal downtime.

4. Validation and Testing

- Data Validation: Compare source and target data to ensure accuracy.

- Use AWS DMS data validation tools.

- Custom scripts for in-depth validation.

- Performance Testing: Perform load testing and performance benchmarking.

- Identify and resolve performance bottlenecks.

- Functional Testing: Ensure all applications function correctly with Aurora PostgreSQL.

- User acceptance testing (UAT).

5. Cutover and Go-Live

- Final Sync: Ensure all data changes are replicated to Aurora PostgreSQL.

- Cutover Planning: Minimize downtime during the switch.

- Go-Live: Switch application connections to Aurora PostgreSQL.

- Post-Migration Support: Monitor and support the new environment.

6. Optimization and Continuous Improvement

- Performance Tuning: Optimize the database for peak performance.

- Cost Optimization: Leverage AWS cost management tools.

- Training and Documentation: Train staff on new systems and update documentation.

IV. Detailed Action Plan

1. Assessment and Planning

- Conduct workshops with stakeholders.

- Document current Oracle environment and identify critical databases.

- Define success criteria and key performance indicators (KPIs).

2. Schema Conversion

- Install and configure AWS SCT.

- Execute schema conversion and review conversion report.

- Resolve any schema conversion issues.

3. Data Migration

- Install and configure AWS DMS.

- Create and test DMS tasks for initial load and continuous replication.

- Monitor DMS tasks and troubleshoot any issues.

4. Validation and Testing

- Develop and execute data validation scripts.

- Perform comprehensive performance and functional tests.

- Document test results and address any issues.

5. Cutover and Go-Live

- Plan cutover steps and communicate with stakeholders.

- Execute final sync and switch application connections.

- Monitor the new environment and provide immediate support.

6. Optimization and Continuous Improvement

- Regularly review and tune database performance.

- Implement AWS cost-saving measures.

- Provide ongoing training and support for staff.

V. Risk Management

1. Data Loss: Ensure robust backup and recovery processes.

2. Downtime: Plan for minimal downtime during cutover.

3. Performance Issues: Conduct thorough performance testing.

4. Compatibility Issues: Address schema conversion and application compatibility issues early.

VI. Timeline and Milestones

1. Phase 1: Planning and Assessment (1 month)

2. Phase 2: Schema Conversion (1 month)

3. Phase 3: Data Migration (2 months)

4. Phase 4: Validation and Testing (1 month)

5. Phase 5: Cutover and Go-Live (1 month)

6. Phase 6: Optimization and Continuous Improvement (Ongoing)

VII. Budget and Resources

1. AWS Costs: Estimate costs for SCT, DMS, and Aurora PostgreSQL.

2. Staffing: Allocate necessary resources for each phase.

3. Training: Budget for training sessions and materials.

VIII. Conclusion

Migrating from Oracle to Aurora PostgreSQL can yield significant benefits in cost, performance, and scalability. By following this strategic plan and leveraging AWS tools, we can ensure a smooth and successful transition while mitigating risks and ensuring compliance.

Next Steps: Secure executive approval, finalize the project team, and initiate the planning phase.