Pilot Migration

Using Triples to add Semantic Value to Database Migration;

AIMLUX.ai PowerGraph Consulting:  Understanding the benefits of Graphixa.ai - Pilot Migration is the "stress test" that proves your three-tiered strategy works before you commit the entire enterprise dataset. Using Graphixa.ai as the semantic orchestrator, alongside mechanical schema tools and human expertise, ensures that you aren't just moving data—you're moving meaning.

 AIMLUX.ai Pilot Migration - Structured checklist :

Phase 1: Preparation & Setup (The "Shell")

Goal: Create the technical destination and the semantic rules.

• [ ] Mechanical: Run the Schema Conversion Tool to generate DDL for a specific subset of tables (e.g., "Customer" and "Transactions" domains).

• [ ] Semantic: Define the Ontology in Graphixa.ai for this pilot scope (semantic types like customer_id, trans_date).

• [ ] Human: Review the converted schema. Does the DDL align with the cloud destination's best practices (clustering keys, partition logic)?

• [ ] Human: Finalize the "Source of Truth" definitions with business owners to ensure the Graphixa ontology is accurate.

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Phase 2: Orchestration & Mapping (The "Brain")

Goal: Link the source to the target without hard-coding.

• [ ] Semantic: Perform Bidirectional Mapping in Graphixa.ai. Map legacy CSV/DB headers to the ontology and the new cloud columns to the same ontology.

• [ ] Human: Manually validate "low-confidence" matches. If Graphixa isn't sure if C_UID is customer_id, an expert must confirm.

• [ ] Semantic: Select the Type-Aware Transformation rules (e.g., "Legacy Date to ISO 8601") for the pilot data.

• [ ] Human: Identify any complex procedural logic (old triggers/stored procs) that the rule-set cannot handle; mark these for manual redesign.

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Phase 3: Execution & Feedback (The "Heartbeat")

Goal: Run the data through the pipes and monitor for clogs.

• [ ] Semantic: Execute the Batch Load. Use Graphixa to generate and run the SQL Upserts for the pilot records.

• [ ] Mechanical: Monitor the cloud DB's ingestion performance. Is the bulk loader hitting any technical bottlenecks?

• [ ] Semantic: Review the Error Feedback Loop. Did Graphixa reject any rows? (e.g., a "text" value found in a "numeric" semantic field).

• [ ] Human: Perform "Root Cause Analysis" on rejected rows. Is the issue in the source data, the ontology definition, or the transformation rule?

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Phase 4: Validation & Lineage (The "Audit")

Goal: Prove that the data arrived correctly and is traceable.

• [ ] Semantic: Generate a Lineage Report in Graphixa.ai for a sample of migrated records. Can you trace Record #502 from the Cloud DB back to the original legacy row?

• [ ] Human: Conduct Functional Validation. Do the pilot reports in the new system match the numbers in the legacy system?

• [ ] Human: Perform Performance Tuning. Does the new SQLScript (redesigned by humans) run faster than the legacy code?

• [ ] Strategic: Make the Go/No-Go Decision for the full-scale migration based on the pilot's error rates and lineage accuracy.