Arcxa architectural sql mapping

Arcxa architectural mapping outlines a highly optimized, modernized data platform designed to break down legacy data silos, reduce licensing costs, and run edge AI workloads without relying on expensive, supply-constrained GPUs.

Here is a breakdown of what the Arcxa stack accomplishes and how the components interact:

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1. Legacy Oracle Stack Migration

• Problem: The top tier represents the starting point—a legacy Oracle environment. These setups are notorious for high licensing fees, vendor lock-in, and fragmented data silos that make it difficult to run modern analytics.

• The Action: Data and workloads are migrated downward into a more open, cost-effective, and scalable ecosystem.

2. ARCXA Semantic Mapping (Data Governance Layer)

• Role: ARCXA acts as the semantic and integration layer. It ensures that data moving out of the legacy silos retains its business context, definitions, and relationships.

• Integrations:

  • Collibra: Manages data governance, cataloging, and data lineage.

  • Informatica: Handles complex enterprise data integration (ETL/ELT) and data quality.

  • Fivetran: Provides automated, zero-maintenance data pipelines to ingest data into the target database.

3. EDB PostgreSQL Stack (Modern Open-Source Database)

• Engine: EnterpriseDB (EDB) PostgreSQL serves as the enterprise-grade destination for the migrated Oracle data. EDB provides Oracle compatibility features (like PL/SQL support), making it significantly easier to migrate schema and code away from Oracle without a total rewrite.

• Foundation: It replaces the expensive legacy database layer with an open-source standard, backed by enterprise support and security.

4. IBM Power10/11 Matrix Math Acceleration (MMA) (The Compute Layer)

• Hardware Innovation: The entire stack is powered by IBM Power architecture (Power10 or the latest Power11).

• GPU-Free Edge AI / LLM Inferencing: IBM Power processors feature built-in Matrix Math Acceleration (MMA) directly on the core. MMA is a hardware accelerator designed specifically for numerical linear algebra, which is the mathematical foundation for deep learning, Large Language Models (LLMs), and AI inferencing.

• The Value Proposition: By leveraging MMA, the system can run AI models and LLM inferencing directly on the database server at the edge or in the data center. This eliminates the need to buy, power, cool, and manage separate NVIDIA or AMD GPUs, drastically reducing infrastructure complexity and latency by keeping the AI compute right next to the data.