Migration Readiness Assessment (MRA) for Arcxa Migration Automation

Migration Readiness Assessment (MRA) for Arcxa Migration Automation structures complex data ecosystems—combining legacy systems of record, cloud analytics targets, and ETL pipelines—into a predictable, low-risk migration plan.

Using Arcxa’s Semantic Control Plane (SCP), the MRA shifts traditional manual SQL rewrites into metadata-driven transformations.

Phase 1: Core Project Baseline (Scope, Goals & Timelines)

1. Scope Definition

• Source Systems Inventory: Map systems of record (Oracle PL/SQL, SAP HANA, IBM DB2).

• Downstream Connections: Identify all attached modern targets (Databricks, Snowflake, AWS) and ETL/data governance tools (Informatica, Collibra, Fivetran).

• Object & Dependency Inventory: Catalog tables, views, stored procedures, triggers, embedded SQL, and active pipeline logic across all legacy databases.

2. Strategic Goals & KPIs

• Data Fidelity: Zero-loss semantic transformation (preserving business logic embedded in PL/SQL or DB2 packages).

• Pipeline Modernization: Replace custom ETL scripts with declarative SPO (Subject-Predicate-Object) RDF graph mapping rules.

• Cost & Time Reduction: Target up to 10x speedup in schema reconciliation and reduction in post-migration hypercare costs.

Phase 2: Enterprise Systems & Ecosystem Mapping Matrix

Use this registration structure to log all technical endpoints across On-Prem, Cloud, and Hybrid Cloud environments:

3. Phased Timeline & Execution Milestones

Phase	Duration	Key Deliverables & Arcxa Tooling
Phase 1: Discovery & Profiling	Weeks 1–2	Auto-schema ingestion & entity discovery via arcxa-coordinator and arcxa-cli.
Phase 2: Semantic Mapping	Weeks 3–4	Domain ontology definition & R2RML rules via arcxa-model-service.
Phase 3: Pipeline Integration	Weeks 5–6	Integration with ETL/Governance tools (Collibra, Informatica, Fivetran) & MCP servers.
Phase 4: Dry-Run & Lineage	Weeks 7–8	Execution dry-runs via arcxa-shard and lineage verification in the Arcxa Operator UI.
Phase 5: Cutover & Go-Live	Week 9+	Production cutover, validation sign-off, and active context serving.

Phase 3: Industry & Regulatory Compliance Alignment

When migrating regulated workloads, legacy database schemas contain sensitive structures that must be mapped to automated security and compliance standards:

Arcxa automates the mapping of legacy database schemas to security and compliance standards by shifting compliance from manual, post-migration audits into an automated, metadata-driven transformation pipeline.

Instead of rewriting database security rules manually for every target platform, Arcxa uses its Semantic Control Plane (SCP) to translate legacy data structures, constraints, and business rules into standardized semantic models. Here is how this automated compliance mapping is executed:

Industry / Regulatory Mandate	Compliance Requirement	How Arcxa Automation Assists
Financial Services (SOX / PCI-DSS)	Cryptographic data traceability, immutable audit logs, strict segregation of duties.	Defensible Lineage: Provides graph-native lineage tracking (row, column, schema evolution) to explain every migrated value.
Healthcare (HIPAA / HITECH)	Protection of Protected Health Information (PHI), encryption at rest/in transit, zero data leakage.	Zero-Trust Access & SHACL Validation: Enforces access policies directly against SPO predicates before data moves to target environments.
Cross-Industry / Global (GDPR / CCPA)	Right to be Forgotten, data residency controls, and metadata cataloging.	Collibra Integration: Synchronizes Arcxa’s active metadata layer with Collibra catalogs to maintain continuous data governance and lineage visibility.

Phase 4: Step-by-Step MRA Execution Workflow

1. Automated Schema Ingestion & Inventory

   • Register legacy connection endpoints with the Arcxa Coordinator.

   • Run automated inspection across Oracle, SAP HANA, and IBM DB2 to extract tables, foreign keys, stored procedures, and triggers.

2. Ontology & SPO Rule Definition

   • Map relational schemas into standardized Subject-Predicate-Object (SPO) graph abstractions.

   • Utilize arcxa-model-service to generate R2RML declarative mapping rules, abstracting business logic away from proprietary ETL scripts.

3. ETL & Metadata Orchestration

   • Connect existing pipelines (Informatica, Fivetran) to feed transformed semantic graphs directly into target analytics platforms (Databricks, Snowflake, AWS).

   • Register metadata definitions in Collibra for enterprise-wide data governance compliance.

4. Validation Dry-Run & Lineage Sign-off

• Execute dry-run transformations on arcxa-shard.

• Inspect native lineage in the Arcxa Operator UI to verify that transformed business rules comply with industry regulations and target schema requirements before production cutover.

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SECTION II

Arcxa Migration Readiness Assessment (MRA) Questionnaire:

This assessment tool helps structure the discovery and intake phase for migrating legacy workloads (Oracle PL/SQL, SAP HANA, IBM DB2) into modern cloud targets (Databricks, Snowflake, AWS) using Arcxa Migration Automation.

Section 1: Strategic Scope, Business Goals & Timelines

1.1 Business Context & Objectives

1. What are the primary drivers for this migration? (e.g., legacy license cost reduction, platform modernization, cloud consolidation, performance bottlenecks)

2. What is the target go-live date or hard deadline for deprecating legacy infrastructure?

3. What is the acceptable maintenance/downtime window for the final production cutover?

1.2 Timeline & Phasing Expectations

1. Are you planning a single "Big Bang" cutover or a Phased/Coexistence migration strategy?

2. What are the priority tiers for migration?

• Tier 1 (Critical): Core operational databases / real-time reporting

• Tier 2 (Secondary): Analytical marts / batch reporting

• Tier 3 (Archival): Read-only historical archives

Section 2: Legacy Systems of Record Assessment

2.1 Oracle PL/SQL Systems

1. What version(s) of Oracle Database are in use?

2. How heavily does the system rely on embedded business logic?

   • Estimated count of Stored Procedures:

   • Estimated count of Packages & Triggers:

   • Extent of custom PL/SQL types, object views, or Oracle-specific functions:

3. How is database change management currently handled?

2.2 SAP HANA Systems

1. Is SAP HANA used as an enterprise data warehouse (BW on HANA) or transactional engine (S/4HANA)?

2. Are calculation views, analytic views, or SQLScript procedures heavily utilized?

3. Will the migration extract raw underlying tables or rely on SAP application-level extractors/OData services?

2.3 IBM DB2 Systems

1. What platform hosts the IBM DB2 database? (e.g., z/OS Mainframe, LUW - Linux/Unix/Windows, iSeries)

2. Are there mainframe-specific encoding issues (e.g., EBCDIC conversion requirements)?

3. How many COBOL or embedded SQL programs directly query or mutate DB2 tables?

Section 3: Data Integration & Governance Ecosystem

ETL & Pipeline Architecture

1. Informatica:

   • Version/Deployment (PowerCenter vs. Informatica Intelligent Cloud Services / IICS).

   • How many total workflows/mappings are candidate targets for translation or replacement?

2. Fivetran:

   • Is Fivetran currently used for raw CDC (Change Data Capture) ingestion, or planned for target loading?

3. Collibra / Metadata Management:

• Is Collibra configured as the active enterprise data catalog?

• Are data governance policies, domain ownership, and technical metadata synchronized via Collibra APIs?

Target Analytics & Cloud Platforms:  ,AWS Direct Connect, Azure ExpressRoute, PrivateLink

What is the target destination for each workload?

Databricks: Delta Lake schema target, PySpark translation preference.

Snowflake: Snowflake SQL / Snowpark requirements.

AWS: Amazon Redshift, AWS Glue, or S3/Iceberg data lakehouse strategy.

1. Are hybrid cloud network connections established? (e.g., AWS Direct Connect, Azure ExpressRoute, PrivateLink)

Section 4: Industry & Regulatory Compliance Controls

1. Regulatory Scope: Which regulatory frameworks govern the data being migrated?

   • [ ] Financial Services: SOX, PCI-DSS, BASEL III

   • [ ] Healthcare: HIPAA, HITECH

   • [ ] Global / Privacy: GDPR, CCPA / CPRA

2. Sensitive Data Profiling:

   • Are PII, PHI, or PCI columns mapped and tagged in existing metadata tools?

   • Are column-level encryption or dynamic data masking rules required in transit and in target environments?

3. Auditability & Lineage:

• Is end-to-end column-level lineage tracking required for regulatory audit sign-offs?