AWS MAP for Database Migration: From Oracle and SQL Server to AWS

Homogeneous vs Heterogeneous: Which Path Fits Your Database?

A homogeneous migration moves a database to the same engine on AWS. SQL Server on-premises becomes SQL Server on Amazon RDS. Oracle Database becomes Oracle on RDS. The schema stays identical. Application code rarely changes. Testing focuses on performance validation rather than functional regression.

Heterogeneous migrations change the engine entirely. Oracle becomes Amazon Aurora PostgreSQL-Compatible. SQL Server becomes Aurora MySQL-Compatible or open-source PostgreSQL on RDS. These moves require schema translation, stored procedure conversion, and application-layer changes. They are harder but deliver far greater returns.

According to Forrester's 2023 Total Economic Impact study of Amazon Aurora, organizations that migrated from commercial databases achieved a 186% ROI over three years. The savings come from eliminated licensing fees, reduced DBA overhead, and Aurora's pay-per-use pricing. Heterogeneous migration is where MAP funding has the most impact because the upfront effort is higher.

For a detailed look at how MAP funding works and what qualifies, our sibling guide covers credit tiers and eligibility criteria.

How Does AWS Database Migration Service (DMS) Work?

AWS DMS is the engine behind most MAP-funded database migrations. It creates a replication instance that connects to your source database, reads data, and writes it to the target. DMS supports full-load migration and ongoing change data capture (CDC) for near-zero-downtime cutovers.

For homogeneous migrations, DMS handles the transfer with minimal configuration. You define source and target endpoints, create a replication task, and monitor progress through the AWS console. Schema objects transfer cleanly because the engine is the same on both sides.

Heterogeneous migrations pair DMS with the AWS Schema Conversion Tool (SCT). SCT analyzes your source schema and generates a compatibility report. It highlights code that converts automatically and flags items needing manual intervention. Typical problem areas include Oracle PL/SQL packages, SQL Server T-SQL cursors, and database-specific functions.

DMS also supports migration to non-relational targets. Organizations moving from Oracle to DynamoDB for specific microservices use DMS to extract and transform relational data into key-value or document formats. This pattern works well for read-heavy workloads that benefit from DynamoDB's single-digit-millisecond latency. If you need a dedicated database migration service, Opsio's team can manage DMS configuration end to end.

What Does Migrating Oracle to Aurora PostgreSQL Look Like?

Oracle-to-Aurora migrations represent the most common heterogeneous path under MAP. AWS reports that thousands of commercial database instances have moved to Aurora since the service launched. The process follows a repeatable sequence that MAP partners execute at scale.

First, SCT scans the Oracle schema. It evaluates tables, views, stored procedures, triggers, sequences, and synonyms. SCT generates a detailed action report showing conversion percentages. A well-structured Oracle database typically converts 80–90% automatically. The remaining 10–20% requires manual PL/SQL-to-plpgsql rewriting.

Second, the team converts and validates the schema on Aurora PostgreSQL. Unit tests run against converted stored procedures. Data type mappings are verified—Oracle NUMBER becomes PostgreSQL NUMERIC, CLOB becomes TEXT, and DATE handling requires attention because Oracle DATE includes time while PostgreSQL DATE does not.

Third, DMS performs the data migration. A full load moves all existing data. CDC replication keeps the Aurora target synchronized while the source Oracle instance continues serving production traffic. This allows weeks of parallel running before the final cutover.

Fourth, application teams update connection strings, ORM configurations, and any Oracle-specific SQL syntax. This is where thorough testing matters. Load testing against Aurora validates that query performance meets or exceeds the Oracle baseline.

How Do You Migrate SQL Server to Amazon RDS?

SQL Server migrations to RDS offer a simpler path because RDS natively supports SQL Server. Organizations choosing this homogeneous route keep their existing T-SQL code, SSIS packages, and application integrations intact.

RDS for SQL Server supports Enterprise, Standard, and Web editions. License-included pricing bundles the SQL Server license into the RDS hourly rate. Organizations with existing Software Assurance can use the License Mobility benefit through Bring Your Own License (BYOL) on dedicated hosts for potential savings.

The migration itself uses native SQL Server backup and restore. You create a full backup of your on-premises database, upload it to S3, and restore it onto RDS using the rds_restore_database stored procedure. For large databases exceeding several terabytes, AWS recommends multi-file backups to parallelize the upload and restore process.

For organizations willing to invest more upfront, migrating SQL Server to Aurora PostgreSQL eliminates Microsoft licensing entirely. Gartner estimates that database licensing accounts for 25–40% of total database TCO. Removing that cost layer produces compounding savings year over year. The trade-off is development effort for code conversion, which MAP credits help absorb.

What Are the Common Challenges in MAP Database Migrations?

Schema conversion complexity tops the list. Legacy databases accumulate thousands of stored procedures over decades. Some contain business logic that application teams no longer fully understand. Automated conversion tools handle syntax but miss semantic nuances. Plan for manual code review of converted procedures.

Performance regression is the second risk. Query optimizers differ between engines. A query that runs in 50 milliseconds on Oracle may take 500 milliseconds on PostgreSQL without index tuning. Execution plan analysis on the target engine should begin early and continue through parallel running.

Data type mismatches cause subtle bugs. Oracle's implicit type coercion differs from PostgreSQL's stricter behavior. Applications relying on implicit conversions break silently. Comprehensive integration testing catches these issues before production cutover.

Downtime windows concern every stakeholder. DMS with CDC minimizes downtime to the final cutover window—typically minutes rather than hours. But applications with strict write ordering or distributed transactions need additional coordination. MAP partners with database specialization plan these cutovers down to the minute.

How Should You Structure Your MAP Database Migration Plan?

Start with a portfolio assessment. Catalog every database instance: engine, version, size, connections, licensing cost, and business criticality. Tools like AWS Application Discovery Service and partner solutions automate this inventory. The assessment output becomes your MAP business case.

Prioritize databases by migration complexity and business value. Quick wins—small SQL Server databases moving to RDS—build team confidence and demonstrate MAP ROI early. Complex Oracle estates move later with lessons learned applied.

Build a factory model for repetitive migrations. When you have fifty SQL Server databases of similar architecture, templatize the migration runbook. DMS task configurations, SCT projects, and testing scripts become reusable assets. This factory approach is how MAP partners migrate hundreds of databases within the program timeline.

Invest in training during mobilization. AWS offers MAP-funded training credits for DBA teams learning Aurora, DynamoDB, or RDS management. Operational readiness matters as much as migration execution. Your DBAs need to monitor, patch, and optimize on the new platform from day one.

Why Partner with a MAP-Certified Provider for Database Migration?

Database migrations carry business risk that infrastructure moves do not. A misconfigured VM is an inconvenience. A corrupted database is a business continuity event. MAP-certified partners bring database-specific migration experience that reduces both risk and timeline.

Partners like Opsio hold AWS competencies in database and migration specializations. They have completed MAP engagements across industries, building institutional knowledge about engine-specific gotchas. They know which Oracle features have direct Aurora equivalents and which require architectural redesign.

Partners also manage the MAP administrative process. They handle AWS funding applications, track credit utilization, and ensure your migration meets the program milestones that trigger credit disbursement. This administrative overhead is non-trivial and distracts internal teams from technical execution.

If you are considering how storage migration under MAP can complement your database move, consider running both workstreams in parallel. Data and databases often migrate together, and coordinating both under a single MAP engagement maximizes credit utilization and minimizes total migration duration.

Conclusion

Migrating Oracle and SQL Server databases to AWS under the MAP program is a financially sound strategy that pairs technical tooling with economic incentives. Whether you choose a homogeneous path for speed or a heterogeneous path for long-term savings, MAP funding reduces the barrier to action. DMS and SCT handle the mechanical work. Experienced partners handle the judgment calls. The result is a modern database estate that costs less to operate and scales without licensing constraints.