Tactical Edge
AWS Migration Competency delivery

A reusable toolchain and delivery model for assessing database estates, proving source-to-target behavior, deploying AWS migration controls, and gating cutover with evidence. The first automated path supports Oracle to PostgreSQL.

Reference architecture

Tactical Edge AWS Database Modernization Factory

We use AWS DMS for supported full-load and change data capture paths. When workload scale, engine compatibility, or the downtime model calls for a different method, we use the appropriate engine-native backup, restore, or bulk-load path. Conversion and assurance remain separate, visible layers.

Source estate

Oracle
Db2 and SAP ASE
SQL Server
Teradata and Netezza
MySQL and PostgreSQL

Database Modernization Factory

01

Estate assessment

02

Target evaluation

03

Conversion remediation

04

Business parity testing

05

Cutover coordination

06

Post-cutover stabilization

Migrate

AWS DMS or engine-native transfer

Convert

DMS SC, AWS Transform, AWS SCT

Assure

Factory evidence, controls, approvals

AWS destinations

Aurora PostgreSQL
Amazon RDS
Oracle Database@AWS
Amazon Redshift

Data migration

AWS DMS handles supported full-load and CDC routes. Engine-native backup, restore, export, import, or bulk-load utilities are used when they provide a safer or faster fit.

Schema and application conversion

DMS Schema Conversion, AWS Transform, and AWS SCT address supported conversion work. Tactical Edge resolves remaining database objects and affected application SQL through reviewed changes.

Factory assurance and cutover

The collector, validation harness, CloudWatch checks, Step Functions gate, evidence store, and named approvals prove readiness. Customer-specific freeze, endpoint switch, rollback, and stabilization steps are rehearsed separately.

Reusable Factory accelerators

The tools behind the offering

These reusable assets deploy in a customer-approved environment and produce portable evidence for architecture review, migration testing, and cutover decisions. The current collector and validation path supports Oracle to PostgreSQL. Additional engine paths are introduced after source-specific testing.

Evidence flow

Each stage produces an artifact the next gate can verify.

Fail-closed controls
01

Collect

Inventory JSON

Read-only source evidence

02

Validate

Parity report

Required checks pass

03

Deploy

AWS control plane

Versioned infrastructure

04

Gate cutover

Named decision

Evidence plus approval

Assessment Collector

Runs read-only Oracle and PostgreSQL inventory queries, maps database objects and dependencies, and produces a reviewable AWS target shortlist without storing credentials.

Validation Harness

Executes approved source-to-target checks for business totals and row sets, applies explicit tolerances, and emits a cutover-eligibility evidence report.

AWS Deployment Templates

Creates a DMS full-load-and-CDC task, encrypted evidence storage, approval state, least-privilege functions, logs, and a Step Functions control plane.

Cutover Orchestrator

Checks DMS status, table validation, CDC latency, business-parity evidence, and a named human decision before allowing the cutover gate to pass.

Deliberate safety boundary: the generic orchestrator verifies readiness and approval. It does not stop source writes, change application endpoints, or remove rollback infrastructure. Those irreversible steps stay in the application-specific runbook.

Six delivery workstreams

From incomplete inventory to verified production

Each workstream produces a concrete decision or evidence artifact. Customers can begin with assessment, prove one representative workload in a pilot, then reuse the same controls across migration waves.

Estate assessment

Build a read-only inventory of engines, versions, schemas, procedural code, application connections, jobs, change rates, and reporting dependencies.

Dependency map and migration waves

Target evaluation

Compare rehost, replatform, and refactor paths across Aurora PostgreSQL, RDS, Oracle Database@AWS, and Amazon Redshift.

Target decision with cost and risk

Conversion remediation

Ingest AWS conversion reports, resolve unsupported objects, identify affected application SQL, and route high-risk changes for review.

Source-controlled remediation backlog

Business parity testing

Test data, queries, stored procedures, transactions, business totals, and performance against temporary target environments.

Executable functional-equivalence evidence

Cutover coordination

Configure AWS DMS movement, rehearse sequencing, watch replication lag, record approvals, and preserve explicit rollback points.

Rehearsed cutover and rollback runbook

Post-cutover stabilization

Watch query regressions, locking, connections, storage, backups, recovery, cost, and service parameters for 30 to 90 days.

Production-readiness and optimization report

Supported migration lanes

AWS destination options by source workload

Oracle does not always mean Aurora. A warehouse does not belong on an OLTP target. The assessment compares compatibility, operational model, modernization value, downtime, conversion effort, and cost before recommending a path.

SourceAWS destination optionsOffering laneWhere the work concentrates
Oracle OLTPAurora PostgreSQL, RDS PostgreSQL, RDS for Oracle, or Oracle Database@AWSFlagship pathTarget selection, PL/SQL remediation, application dependencies, and functional proof carry the most value.
Oracle data warehouseAmazon RedshiftAdjacent pathAdds ETL, BI, distribution, sort strategy, workload replay, and performance work to the database move.
Db2 and SAP ASEAurora PostgreSQL or RDS PostgreSQLSpecialist pathDeep procedural logic and limited legacy expertise create high remediation and assurance needs.
SQL ServerAurora PostgreSQL, RDS PostgreSQL, or BabelfishAssurance pathFocus on unsupported applications, complex features, regulated evidence, and independent parity testing.
Teradata, Netezza, and GreenplumAmazon RedshiftWarehouse pathThe migration includes scripts, ETL, BI dependencies, reconciliation, workload management, and tuning.
MySQL and PostgreSQLAurora or RDS, same engineStandardized pathWell suited to version upgrades, consolidation, managed operations, and faster migration packages.

Start with the evidence

The first engagement is a fixed-scope Oracle Exit and AWS Modernization Assessment. It ends with a target decision, migration waves, conversion findings, application impact, downtime architecture, risk estimate, and a pilot scope.

  • Read-only estate and dependency inventory
  • Target recommendation for every workload
  • Conversion and application impact analysis
  • Cutover, rollback, effort, and pilot plan

A confidence score backed by tests

Conversion percentage is not enough. The pilot reports what compiled, what passed functional tests, what matched the source, what met performance targets, and what still needs human remediation.

Illustrative evidence package

Object

compile status

Query

result parity

Business

reconciliation

Workload

performance

Frequently asked questions

Plan the route before starting replication

Does the Database Modernization Factory replace AWS DMS?

No. AWS DMS and DMS Schema Conversion provide managed migration, replication, conversion, and data validation capabilities. The Factory adds reusable assessment, business-parity evidence, AWS deployment templates, and an approval-gated cutover control plane, plus delivery work for application dependencies, unresolved conversions, rehearsals, and stabilization.

Does every Oracle workload move to Aurora PostgreSQL?

No. The assessment compares Aurora PostgreSQL, RDS PostgreSQL, RDS for Oracle, Oracle Database@AWS, and Amazon Redshift based on workload behavior, Oracle-specific dependencies, modernization goals, downtime, risk, and cost.

What does the first engagement deliver?

The fixed-scope assessment delivers a database and application dependency inventory, target recommendation, conversion analysis, application impact report, downtime architecture, migration waves, risk and effort estimate, and a scoped pilot proposal.

How is migration correctness tested?

The validation harness compares approved source and target queries, row sets, and business totals using explicit tolerances. Customer-specific procedure, transaction, application, and workload tests extend that evidence. Required failures block the generic cutover gate, and high-risk exceptions require a named decision.

Bring one representative database

We will assess the source, compare AWS targets, identify the last-mile conversion work, and define a pilot that proves parity before you commit the full estate.

Start the assessment