AI in Database Migration: How DMAP Uses GenAI to Redefine Enterprise Database Modernization

Most enterprises treating database migration as a data-transfer problem are solving the wrong equation. The actual problem is one of semantic equivalence — how do you faithfully translate decades of tightly coupled business logic, embedded procedural code, and proprietary database behaviours from one execution engine to another, at enterprise velocity, without breaking mission-critical applications?

Conventional tooling answers this question poorly. Rule-based converters handle DDL and surface-level DML, then collapse when they encounter the structural complexity of real Oracle estates: nested cursor loops, vendor-specific analytical functions, proprietary scheduler integrations, and Oracle RAC-dependent concurrency patterns. The remainder gets handed to senior engineers who spend months remediating what automated tools couldn’t resolve — burning budget, extending timelines, and accumulating technical debt before the first query even hits PostgreSQL in production.

GenAI changes this calculus fundamentally. Not by replacing engineers, but by embedding contextual intelligence — semantic reasoning, adaptive learning, and agentic self-correction — directly into the migration execution pipeline. Newt Global’s DMAP (Database Modernization Acceleration Platform) is the most technically sophisticated expression of this approach in the market today. This article deconstructs exactly how DMAP deploys GenAI across the full migration lifecycle — from AST-level code parsing to agentic recursive correction loops — and what that means for enterprises planning large-scale Oracle-to-PostgreSQL modernization in 2026.

1. Why Legacy Migration Tools Fail: A Structural Analysis

The root failure of legacy migration toolkits is architectural, not merely a feature gap. Tools like AWS Schema Conversion Tool (SCT) and open-source utilities like ora2pg were designed for a narrower version of the migration problem: schema translation. They operate on a deterministic pattern-substitution model — enumerate known Oracle constructs, define transformation rules, apply them sequentially. This works reliably for simple DDL (table definitions, index declarations, sequence structures) and straightforward DML.

It fails — categorically — when confronted with the actual composition of production Oracle estates:

• Procedural logic density: In large Oracle environments, manual PL/SQL remediation routinely exceeds 60% of total migration effort. Packages containing thousands of lines of tightly coupled business logic cannot be mechanically translated; they must be semantically understood.
• Oracle-proprietary constructs without PostgreSQL equivalents: CONNECT BY PRIOR (hierarchical queries), DBMS_SCHEDULER jobs, SYS_REFCURSOR patterns, Oracle Advanced Queuing, and DBMS_OUTPUT all require contextual refactoring — not rule substitution.
• Implicit type coercions: Oracle’s permissive implicit casting (e.g., NUMBER treated silently as VARCHAR2 in certain contexts) generates code that passes static analysis but produces wrong results under PostgreSQL’s stricter type discipline.
• Hidden application-layer dependencies: JDBC/ODBC connection strings, ORM mappings in Java/.NET applications, and embedded SQL in Python scripts form a dependency surface that schema-only tools never inspect.

The consequence: migrations using legacy tooling stall in remediation. Engineers inherit a partially converted codebase full of low-confidence objects that require expert review — precisely the expertise bottleneck that motivated the migration project in the first place. For a full architectural breakdown of why this happens, see: End-to-End Database Migration Architecture Explained by Newt Global Experts.

2. GenAI as Migration Intelligence: Beyond Pattern Matching

The term “AI-powered migration” is now applied so liberally that it has lost meaningful content. For the purposes of this analysis, let’s establish a precise definition: GenAI in data migration refers to the application of large language models and generative systems to achieve four capabilities that rule-based tools structurally cannot provide.

2.1 Semantic Code Comprehension

GenAI models trained on source code corpora — including Oracle PL/SQL, PostgreSQL PL/pgSQL, Java, and .NET — can infer the intent of code, not just its syntax. When an Oracle package uses DBMS_OUTPUT.PUT_LINE for conditional debug tracing inside a production stored procedure, a GenAI model understands that the semantically correct PostgreSQL equivalent is not a direct function call substitution but a restructured logging pattern adapted to PostgreSQL’s execution and transaction model. This distinction — intent vs. syntax — is the entire gulf between a useful tool and a hazardous one.

2.2 Contextual Refactoring, Not Just Translation

Idiomatic PostgreSQL is not Oracle PL/SQL with different keywords. A cursor loop that processes rows sequentially in Oracle is a performance anti-pattern in PostgreSQL — the correct refactoring is a set-based operation that eliminates the loop entirely and executes as a single SQL statement. GenAI identifies these structural transformation opportunities because it understands execution semantics, not just token sequences. DMAP’s ML-powered refactoring models propose equivalent constructs — window functions, JSON operators, table-valued parameters — with context-aware optimization informed by the workload classification of the target schema.

2.3 Adaptive Learning Across Migration Iterations

Unlike static rule engines, GenAI systems improve with data. Every DMAP migration execution contributes to a continuously refined knowledge base of successful conversion patterns. Novel Oracle constructs encountered in one migration become training signal for future corrections — meaning DMAP’s conversion accuracy increases monotonically with deployment scale.

2.4 Generative Test Synthesis

One of the least discussed but highest-value applications of GenAI in migration is automated test generation. DMAP’s AI synthesizes behavioural unit test harnesses — complete with randomised input sets, boundary condition vectors, and expected output assertions — for every stored procedure and function it converts. This transforms QA from a manual bottleneck into an automated verification layer, without requiring engineers to write a single test case.

For the full technical characterisation of these capabilities, see: DMAP AI in Database Migrations — Deep Technical Overview.

3. DMAP’s Hybrid Parsing Engine: AST + Semantic Inference

DMAP’s code conversion engine is built on a hybrid parsing architecture that combines two fundamentally different computational approaches — and the interaction between them is what produces conversion quality that neither approach achieves alone.

3.1 Grammar-Based Dialect Parsers

The first layer consists of grammar-based parsers constructed specifically for each source SQL dialect: Oracle PL/SQL, SQL Server T-SQL, and DB2 SQL PL. These parsers are not generic SQL parsers; they are dialect-aware engines that fully model each vendor’s procedural extensions, type systems, exception hierarchies, and control flow constructs. The output is an Abstract Syntax Tree (AST) — a structured representation of both the syntax and the object relationships within a code unit.

The AST captures what pattern-matching tools entirely miss: the relational structure of code. A CONNECT BY PRIOR clause isn’t just a keyword to substitute — it’s a hierarchical traversal operator whose semantics must be preserved in the equivalent WITH RECURSIVE CTE, including anchor member definition, recursive member join conditions, and cycle detection logic.

3.2 AI-Powered Semantic Inference Layer

Layered on top of the AST is DMAP’s GenAI semantic inference engine. Where the grammar parser captures structure, the semantic layer captures intent. This is where constructs that have no direct PostgreSQL equivalent — Oracle’s CONNECT BY, hierarchical PRIOR references, package-level global variables — are resolved through generative reasoning rather than lookup substitution.

Crucially, the semantic layer also performs workload classification: analysing Oracle AWR/ASH execution reports, SQL plan baselines, and schema metadata to categorise target workloads as OLTP, analytical, spatial, time-series, messaging, or AI-vector. This classification drives downstream decisions about PostgreSQL extension selection — pgvector for semantic feature workloads, PostGIS for spatial data, TimescaleDB for time-series, Citus for distributed analytics — ensuring the target architecture is optimised for the actual workload pattern, not just structurally equivalent to the source.

Explore how this workload-aware approach maps Oracle patterns to the right PostgreSQL architecture: Why PostgreSQL is the Future of Databases — and How DMAP AI Accelerates Oracle Migration.

4. Agentic AI and Recursive Compilation Loops

The most architecturally innovative element of DMAP is its agentic AI layer with recursive compilation feedback — a self-correcting execution model that represents a genuine departure from the migration industry’s prior art.

Traditional migration tools follow a one-pass pipeline: parse → transform → output. If the output has errors, the tool flags them and stops. Engineers inherit a list of unresolved objects and begin manual remediation. DMAP operates on a fundamentally different model:

1. Initial Deterministic Conversion: DMAP’s rule-based engine converts all constructs it can handle with high confidence — standard DDL, straightforward DML, well-defined data type mappings.
2. Compilation Submission: The converted code units are submitted to a live PostgreSQL compilation environment for syntax and type validation.
3. Structured Error Capture via MCP: Compilation errors are intercepted by a Model Context Protocol (MCP) server, which structures diagnostic payloads — error type, line reference, object context, dependency chain — into a format consumable by the AI agent layer.
4. Agentic Reasoning and Rewrite: AI agents receive the structured diagnostic context and autonomously determine the correct remediation strategy. An INVALID_IDENTIFIER error on an Oracle package-level variable invocation triggers the agent to restructure the variable as a PostgreSQL session-level parameter. A compilation failure on CONNECT BY syntax triggers the generation of a fully qualified WITH RECURSIVE CTE with appropriate anchor and recursive members.
5. Iterative Resubmission: The corrected code is recompiled. The loop continues until the object compiles successfully and satisfies DMAP’s conformance criteria for PostgreSQL best practices.

This recursive self-correction mechanism means the system handles novel Oracle constructs it has never encountered before — not by pattern lookup, but by reasoning. The automation depth achieved through this architecture is what enables DMAP to consistently reach 90%+ automation rates on complex enterprise estates. For the complete technical specification: Agentic AI for Database Migration: How DMAP Automates Oracle & SQL Server to PostgreSQL.

5. Deep PL/SQL-to-PL/pgSQL Conversion: Package Decomposition and Procedural Refactoring

Oracle’s package construct — combining specification (public interface) and body (implementation) sections — has no direct equivalent in PostgreSQL. PostgreSQL uses schemas for logical namespace organisation, which requires DMAP to perform genuine architectural decomposition rather than syntactic translation.

Consider a representative Oracle package:

-- Oracle PL/SQL Package Specification
CREATE PACKAGE emp_pkg AS
  g_default_dept  NUMBER := 10;  -- package-level global variable
  FUNCTION get_salary(p_emp_id NUMBER) RETURN NUMBER;
  PROCEDURE update_salary(p_emp_id NUMBER, p_new_sal NUMBER);
END emp_pkg;

DMAP’s GenAI engine decomposes this into PostgreSQL equivalents that preserve semantic behaviour:

-- PostgreSQL equivalent (DMAP-generated)
CREATE SCHEMA emp_pkg;

-- Package-level global variable → PostgreSQL custom configuration parameter
-- Set via: SELECT set_config('emp_pkg.g_default_dept', '10', false);
-- Read via: current_setting('emp_pkg.g_default_dept')::INTEGER

CREATE OR REPLACE FUNCTION emp_pkg.get_salary(p_emp_id INTEGER)
RETURNS NUMERIC AS $$
BEGIN
  -- Semantically equivalent logic with Oracle-to-PG type mapping
END;
$$ LANGUAGE plpgsql;

CREATE OR REPLACE PROCEDURE emp_pkg.update_salary(p_emp_id INTEGER, p_new_sal NUMERIC)
LANGUAGE plpgsql AS $$
BEGIN
  -- Explicit transaction control preserved
END;
$$;

This illustrates the depth of transformation required — and why GenAI is essential. The package-level global variable has no syntactic equivalent in PostgreSQL; DMAP’s semantic reasoning engine determines that the correct architectural pattern is a PostgreSQL custom configuration parameter, maintaining accessibility across the function scope while conforming to PostgreSQL’s execution model.

At scale, DMAP handles the full Oracle-to-PostgreSQL construct mapping:

For the complete technical guide to PL/SQL conversion challenges and solutions, see: PL/SQL to PostgreSQL Migration Guide: Challenges, Tools & Best Practices — Newt Global.

6. Dependency Graph Modelling and What-If Simulation

Hidden dependency failures at cutover are the single most common cause of unplanned migration downtime. They occur because most migration tools operate on object-level isolation — converting each stored procedure, view, or function as an independent unit — without modelling the relational graph of dependencies that connects them.

DMAP constructs a comprehensive multi-plane dependency graph before any migration unit executes. The graph covers three dependency planes simultaneously:

• Schema dependency plane: Full directed acyclic graph of views → functions → procedures → packages → triggers, with cardinality and call frequency annotations derived from Oracle AWR data.
• Job dependency plane: ETL workflow chains, Oracle DBMS_SCHEDULER job networks, batch job interdependencies, and cron-equivalent schedules mapped to their PostgreSQL equivalents.
• Application dependency plane: JDBC/ODBC connection pool configurations, ORM entity mappings, embedded SQL in Java, .NET, and Python application codebases — surface areas that schema-only tools entirely miss.

On this three-plane graph, DMAP’s AI engine runs impact simulation before cutover sequencing: “If this PL/SQL package migration is delayed by one sprint, which downstream views, ETL jobs, and application endpoints become invalid?” The system generates impact reports with dependency chain visualisations — enabling migration architects to make informed sequencing decisions rather than discovering breakpoints through production incidents.

This dependency intelligence is also what enables DMAP to generate its migration sequencing schedule automatically — ordering migration units topologically through the dependency graph to guarantee that no object is cut over before its dependencies are validated.

7. Migration-as-Code: YAML Orchestration and CI/CD-Native Pipelines

Enterprise migrations that run as ad-hoc scripts are fundamentally unrepeatable. Every execution is a snowflake — undocumented decisions, inconsistent environments, no rollback path, and no audit trail. DMAP addresses this through a migration-as-code paradigm that treats database migration with the same engineering rigour as application deployment.

The implementation comprises three layers:

7.1 Declarative YAML Migration Specifications

Every migration unit — schema slice, code package, data segment — is defined as a declarative YAML specification. These specifications encode the source object, target equivalent, transformation parameters, validation criteria, and rollback conditions in a version-controllable, reviewable format.

7.2 AI-Powered Pipeline Scheduling

DMAP’s AI pipeline scheduler consumes the YAML specifications and the dependency graph to generate an optimised execution sequence. The scheduler minimises peak-load conflicts, maximises parallel execution where dependency constraints permit, and identifies the optimal cutover timing window based on historical system load patterns from Oracle AWR reports.

7.3 Automatic Rollback Checkpoints

Logical transaction boundaries within the migration pipeline are automatically identified and instrumented as rollback checkpoints. If any validation gate fails post-cutover, the system can roll back to the immediately preceding checkpoint — not to a full pre-migration state — dramatically reducing mean-time-to-recovery for cutover incidents.

The result is a migration execution model that is repeatable, auditable, and CI/CD-ready — every run produces the same deterministic output from the same specification, and every decision is captured in an immutable log. For details on DMAP’s cloud-native migration model: DMAP: Streamlining Oracle to PostgreSQL Migration with Innovative Features.

8. Zero Downtime via CDC, Logical Replication, and AI-Sequenced Cutover

Zero-downtime migration for live production Oracle databases is a legitimate engineering achievement, not a marketing claim — but it requires a specific architecture, not just good intentions. DMAP implements a dual-write synchronisation model that allows Oracle and PostgreSQL to run concurrently through the migration window.

The architecture operates as follows:

• Phase 1 — Logical Replication Establishment: DMAP establishes Oracle LogMiner-based or Debezium CDC capture on the source Oracle instance. Every committed transaction is captured as a structured change event — inserts, updates, deletes, DDL — and streamed to the target PostgreSQL instance in near-real-time.
• Phase 2 — Parallel Bulk Load: While CDC synchronises ongoing transactions, DMAP executes a parallelised bulk load of historical data using containerised loaders that scale horizontally on demand. Row count and checksum validation gates confirm load completeness before the next phase.
• Phase 3 — Lag Convergence Monitoring: DMAP monitors replication lag continuously. When lag falls below a configurable threshold (typically sub-second), the system signals cutover readiness.
• Phase 4 — AI-Sequenced Cutover: The AI pipeline scheduler executes a choreographed cutover sequence — application connection pool rotation, sequence value synchronisation, final constraint enablement, and validation gate execution — in the minimum possible time window, with automatic rollback triggers if any validation fails.

This approach eliminates the “freeze-dump-restore” paradigm that historically made downtime windows unavoidable. The full technical treatment of this architecture: Zero Downtime Database Migration: Is It Possible? — Newt Global.

For organisations migrating to AWS specifically, DMAP integrates natively with AWS DMS and SCT to extend this zero-downtime model into the AWS ecosystem: Oracle to PostgreSQL Migration on AWS: A Complete Modernization Guide.

9. Automated Validation: Behavioural Unit Tests, Hash Equivalence, and Confidence Scoring

Migration validation is where most programmes fail silently. Manual QA cycles cannot achieve comprehensive coverage across a large Oracle estate — they test the paths that engineers think to test, not the paths that will fail in production. DMAP’s automated validation framework eliminates this coverage gap through three complementary validation mechanisms.

9.1 Hash-Based Data Equivalence Testing

For every migrated table, partition, and index, DMAP executes hash-based equivalence validation — computing cryptographic checksums over the data at row, partition, and aggregate levels, then comparing Oracle and PostgreSQL results. Any divergence triggers an immediate alert with the specific row range or partition boundary where data fidelity breaks down.

9.2 Behavioural Unit Test Synthesis

DMAP’s GenAI engine synthesises behavioural unit test harnesses for every converted stored procedure and function. These harnesses include:

• Randomised input generation covering the full domain of each parameter’s data type
• Boundary condition vectors (nulls, zero values, maximum precision numerics, empty strings)
• Golden output sets generated by executing the test harness against the source Oracle system and using the results as the authoritative expected output for PostgreSQL validation

This “golden test” approach guarantees semantic equivalence — not just syntactic correctness — because it validates that Oracle and PostgreSQL produce identical outputs for identical inputs across the full test input space.

9.3 Confidence Scoring and Engineer Review Prioritisation

Each converted object is assigned a confidence score reflecting the AI model’s certainty about the semantic accuracy of the conversion. High-confidence objects proceed through automated validation without engineer review. Low-confidence objects — those involving novel constructs, complex multi-level cursor patterns, or autonomous transaction logic — are surfaced to engineers with a structured remediation context: what was converted, why the conversion is uncertain, and what specific aspects require review.

This confidence-scored triage reduces remediation engineer workload by up to 70% — engineers focus on the 5–10% of objects that genuinely require human judgement, rather than reviewing the entire converted codebase indiscriminately. For the migration validation session delivered at PG Conf India 2026, see: Oracle-to-PostgreSQL Migration Validation with Agentic AI — Newt Global Blog.

10. GenAI for PII Masking, Compliance Mapping, and Audit Governance

For regulated industries — financial services, healthcare, aviation, government — database migration is not merely a technical exercise. It is an event that must satisfy HIPAA, SOX, GDPR, and PCI DSS audit requirements across every phase. DMAP integrates GenAI into the compliance dimension of migration with the same depth it brings to code conversion.

10.1 Intelligent PII Discovery and Masking

DMAP’s GenAI layer performs semantic analysis of schema metadata and column-level data samples to identify PII — not just by column name pattern matching (“email”, “ssn”) but by analysing data distributions and structural signatures. Once identified, PII is automatically masked in non-production migration environments using format-preserving anonymisation — ensuring that development and testing environments contain realistic but legally compliant data throughout the migration lifecycle.

10.2 Schema-Level Confidentiality Protection

For organisations with data sovereignty requirements, DMAP offers GenAI-powered masking of schema structure during the assessment phase. This enables Newt Global’s migration team to conduct comprehensive analysis without exposure to the client’s sensitive schema topology — a critical requirement for defence, government, and financial services engagements.

10.3 Immutable Audit Logs and Compliance Mapping

Every automated conversion decision, every confidence score, every human override, and every validation result is captured in a tamper-evident audit log. DMAP’s compliance mapping module aligns these logs against HIPAA and SOX control requirements, generating auditor-ready evidence packages that demonstrate data handling governance throughout the migration.

For the GenAI-GitHub Copilot integration approach to migration intelligence, see: Revolutionize Migration with GitHub Copilot & DMAP — Newt Global.

11. Multi-Cloud Deployment Architecture: AWS, Azure, and GCP

DMAP’s containerised, horizontally scalable architecture means it is cloud-agnostic by design — not by abstraction, but by native integration with each major cloud platform’s migration ecosystem.

• AWS: DMAP integrates natively with AWS DMS and SCT, extending its AI-powered conversion capabilities into AWS-managed migration workflows. Available on AWS Marketplace for streamlined enterprise procurement. Supports target deployment to Amazon RDS for PostgreSQL and Amazon Aurora PostgreSQL.
• Azure: DMAP is available through the Azure Marketplace and integrates with Azure Database Migration Service. The platform’s GenAI layer is also optimised for Azure-hosted GenAI API endpoints, enabling low-latency AI inference within the Azure network boundary — critical for financial services clients with strict data residency requirements.
• GCP: DMAP supports Cloud SQL for PostgreSQL and AlloyDB as target platforms on GCP. The GCP deployment model leverages GCP Traffic Capture for pre-migration load characterisation, enabling precise IOPS and throughput sizing for the target Cloud SQL instance. For the complete GCP migration architecture, see: Oracle to PostgreSQL Migration on GCP — Deep Dive Architecture Guide.

Across all three clouds, DMAP’s migration-as-code framework delivers the same declarative YAML specification model, the same agentic correction loops, and the same automated validation framework — ensuring consistent migration quality regardless of target cloud. The multi-cloud modernisation strategy is detailed at: Database Modernization Consulting and Multi-Cloud Automation — Newt Global 2026.

12. Enterprise Outcomes: Measured Results at Production Scale

Architecture claims require production validation. DMAP’s GenAI-powered migration capabilities have been executed against some of the world’s most complex Oracle environments, producing measurable outcomes that set a new benchmark for the industry.

LATAM Airlines: 98% Automation on a 17TB Oracle Estate

LATAM Airlines’ Oracle environment represented one of the most demanding migration candidates in the aviation sector — a multi-terabyte estate with deep PL/SQL procedural logic, complex inter-schema dependencies, and zero tolerance for service interruption across a global airline operation. DMAP achieved a 98% automation rate, meaning only 2% of migrated objects required any manual DBA intervention. The migration was delivered on time and under budget — an outcome that directly validates the practical effectiveness of DMAP’s agentic correction and confidence-scored triage at production scale.

12 Months to 5 Months: Timeline Compression

A representative complex Oracle migration estimated at 12 months using conventional manual approaches was executed by DMAP’s automated pipeline — including GenAI-driven code conversion, three-plane dependency analysis, automated test synthesis, and parallel CDC-based cutover — in 5 months. Post-migration, performance regression testing flagged two queries exceeding baseline latency thresholds; DMAP’s AI immediately surfaced index recreation and query rewrite recommendations that resolved both issues without manual DBA intervention.

Benchmarked Performance Advantage

In head-to-head benchmarks against competing migration platforms across the critical enterprise requirements — automation depth, PL/SQL conversion accuracy, zero-downtime capability, compliance controls, and multi-cloud support — DMAP outperforms alternatives by more than 50%. For the full benchmark methodology and results: Best Oracle Migration Tool with Automation in 2026.

13. The Forward Trajectory: Persistent Learning, Parallelised Agents, and Application-Layer GenAI

DMAP’s current production capabilities — agentic recursive correction, hybrid AST parsing, confidence-scored triage, migration-as-code orchestration — represent the state of the art in 2026. The next phase of development is already defined, and it extends the GenAI application surface significantly further.

13.1 Persistent Cross-Migration Learning

The next iteration of DMAP’s learning model moves beyond per-deployment improvement to persistent cross-migration learning. Successful agentic corrections from every enterprise deployment are evaluated for promotion back into the deterministic rule engine — creating a compounding intelligence flywheel where each migration makes every subsequent migration more accurate. For organisations running multi-year database modernisation programmes across dozens of Oracle instances, this creates a measurable and accelerating productivity advantage over time.

13.2 Parallelised Agentic Loops at Hyperscale

For the largest Oracle estates — millions of database objects, thousands of PL/SQL packages — sequential agentic correction is the remaining throughput bottleneck. DMAP’s roadmap includes fully parallelised agent execution, where hundreds of independent agentic loops process separate object clusters simultaneously, constrained only by the dependency graph’s topological ordering.

13.3 Application-Layer GenAI Refactoring

The logical completion of database migration is application-layer modernisation — automatically detecting and remediating Oracle-specific SQL patterns embedded in Java, .NET, and Python application codebases. DMAP’s source code migration services already address this manually; the next phase deploys GenAI to automate the detection and refactoring of CONNECT BY patterns in JDBC queries, Oracle hint syntax in Hibernate mappings, and ROWNUM-based pagination logic in Spring Data repositories. For the broader open-source AI model strategy: Open-Source LLMs in Database Migration — Newt Global.

13.4 Autonomous Post-Migration Optimisation

Beyond cutover, the next frontier of AI in database migration is continuous post-migration optimisation — AI agents that monitor PostgreSQL query performance against pre-migration Oracle baselines, identify regressions as they emerge under real production load, and propose index adjustments, query rewrites, and PostgreSQL planner configuration changes autonomously. Migration, in this model, is not a project with an end date; it is the beginning of a continuously optimising data platform managed by AI. Newt Global’s vision for this future was articulated at AIOUG Multicloud AI World 2026: GenAI-Driven Cloud & Data Migration — Newt Global at AIOUG 2026.

Next Step: Book a DMAP AI Assessment

The technical depth described in this article is not aspirational — it is running in production across enterprise Oracle environments today. LATAM Airlines, Nabors Industries, American Airlines, and Hughes Network Systems have all executed migrations at this level of AI-powered automation. The question for your organisation is not whether this technology works. It is whether your current migration strategy is positioned to benefit from it.

A DMAP AI Assessment begins with a deep scan of your Oracle estate — schema complexity, PL/SQL volume, dependency topology, and application integration surface — and produces a migration readiness report with a fixed-cost, fixed-timeline migration plan. No approximations. No hidden surprises. No maintenance-window planning over a long weekend.

→ Explore DMAP AI at newtglobal.com/dmap

→ Book a Migration Expert Consultation at newtglobal.com

Further Technical Reading from the Newt Global Blog:

• Oracle to PostgreSQL Migration in Weeks with DMAP AI
• DMAP™ Oracle to PostgreSQL Conversion Tool — Technical Overview
• Oracle to PostgreSQL Migration Tool: Schema, Data & Cloud Strategy
• PL/SQL to PL/pgSQL: Migration Challenges & Solutions
• The Rise of Vector DB Technology in Database Migration
• Newt Global at PG Conf EU 2025: Cloud Modernization with DMAP AI