Aurora Migration

Enterprise Work Project

Led the seamless migration of critical databases from Microsoft SQL Server to PostgreSQL.

Project Overview

●I led the migration of a large-scale enterprise application from Microsoft SQL Server to Aurora PostgreSQL on AWS.
●The migration goal was to improve scalability, performance, and long-term maintainability while supporting high-volume merchants and production traffic.
●The project involved comprehensive schema redesign, query refactoring, and performance optimization to achieve approximately 30% improvement in query response times.
●The migration supports over 200,000 merchants in production with zero data loss and improved system reliability.

●The existing Microsoft SQL Server database was limiting scalability and performance as the application grew to support hundreds of thousands of merchants.
●Migration to a cloud-native database solution was necessary to improve long-term maintainability and reduce infrastructure costs.
●The migration required zero data loss and minimal downtime to avoid disrupting production merchant operations.
●Application code needed refactoring to support PostgreSQL syntax and leverage new database features for optimal performance.

●I designed a comprehensive migration strategy that included schema redesign to optimize for PostgreSQL's strengths and data type mapping from MSSQL to PostgreSQL.
●The migration involved converting stored procedures, functions, and triggers from T-SQL to PostgreSQL PL/pgSQL syntax.
●I implemented a phased approach to minimize risk, starting with non-critical tables and gradually migrating core transaction tables.
●Data migration scripts were developed to ensure accurate data conversion, including handling of MSSQL-specific data types and constraints.
●Index optimization was performed to leverage PostgreSQL's indexing capabilities and improve query performance.

●I redesigned the database schema to take advantage of PostgreSQL features, including proper use of JSONB for flexible data storage and optimized foreign key relationships.
●Application code was refactored to replace MSSQL-specific syntax with PostgreSQL-compatible SQL, including date functions, string operations, and aggregate functions.
●I converted stored procedures from T-SQL to PostgreSQL PL/pgSQL, ensuring business logic remained intact while adapting to PostgreSQL syntax requirements.
●Query optimization involved analyzing execution plans, creating appropriate indexes, and rewriting complex queries to leverage PostgreSQL's query optimizer.
●I implemented data migration scripts that handled type conversions, constraint mappings, and ensured referential integrity throughout the migration process.
●Application-level changes were made to support the new database connection strings, error handling, and transaction management specific to PostgreSQL.

●I optimized database indexes to align with PostgreSQL's B-tree and GIN index types, improving query performance for frequently accessed data.
●Query refactoring focused on eliminating MSSQL-specific optimizations and leveraging PostgreSQL's advanced query planning capabilities.
●I implemented connection pooling and query caching strategies to reduce database load and improve response times.
●The migration resulted in approximately 30% improvement in query response times compared to the previous MSSQL implementation.
●Performance testing was conducted under production-like load to validate optimization improvements and identify bottlenecks.

●I conducted comprehensive regression testing to ensure all application functionality worked correctly with the new database.
●Data consistency validation was performed to verify zero data loss and accurate data migration across all tables.
●I implemented automated testing scripts to compare query results between MSSQL and PostgreSQL implementations during the migration phase.
●Transaction integrity was validated to ensure ACID properties were maintained throughout the migration process.
●Performance benchmarks were established and monitored to ensure the migration met performance improvement goals.
●Rollback procedures were prepared and tested to ensure the ability to revert to MSSQL if critical issues were discovered.

●The migration supports over 200,000 merchants in production, handling high-volume transaction processing reliably.
●Query performance improvements of approximately 30% contribute to better user experience and reduced system load.
●The move to Aurora PostgreSQL provides better scalability for future growth and reduces infrastructure management overhead.
●Improved system reliability and performance enable the platform to handle increased merchant traffic without degradation.

●Migrated a large-scale enterprise application from MSSQL to PostgreSQL, handling schema design, data conversion, and query optimization to improve query response time by ~30%.
●Refactored application code to support the new database, ensuring seamless integration and improving system scalability and performance.
●Conducted regression testing and validation to ensure zero data loss and improved query efficiency post-migration.
●Delivered migration and feature enhancements that support over 200K merchants, improving transaction reliability and scalability.