Swarovski – Global Data Lakehouse & AI-Led Migration

1. Executive Summary & Problem Solved

Swarovski faced significant data fragmentation caused by legacy SAP BW instances and distributed supply chain databases. High licensing overheads, delayed reporting cycles, and labor-intensive manual Dataform SQL development hindered operational agility. CLOUDSUFI executed a global migration to a Google Cloud BigQuery Data Lakehouse, using custom AI Data Agents to automate code translation, schema modeling, DAG optimization, and root-cause remediation.

2. Technical Stack & Medallion Architecture

• Storage & Query Layer: Google BigQuery structured according to a Medallion architecture (Bronze raw ingestion, Silver cleansed/conformed entities, Gold business consumption layers).
• Orchestration & Transformation: Cloud Composer (managed Apache Airflow) for workflow orchestration and Dataform for SQL-based transformation modeling.
• Compute & Integration: Cloud Functions for serverless event-driven ingestion, Looker for enterprise BI semantic modeling and dashboarding, and a custom React UI for administrative configuration.

3. AI Data Agents Architecture & Roadmap

To accelerate the enterprise migration and ensure long-term operational resilience, CLOUDSUFI deployed a three-layer AI Agent architecture:

                                SWAROVSKI AI AGENT ARCHITECTURE ROADMAP
 ┌────────────────────────────────────────────────────────────────────────────────────────────────────────┐
 │ LAYER 3: AUTONOMOUS OPS (Operational Peak)                                                             │
 │ 40% Reduction in Manual Interventions                                                        │
 │ ┌───────────────────────────────────────────────┐  ┌─────────────────────────────────────────────────┐ │
 │ │ DAG Refinery (Predicts failures/resources)    │  │ TraceBack Resolver (Root cause self-healing)    │ │
 │ └───────────────────────────────────────────────┘  └─────────────────────────────────────────────────┘ │
 └───────────────────────────────────▲────────────────────────────────────────────────────────────────────┘
                                     │
 ┌───────────────────────────────────┴────────────────────────────────────────────────────────────────────┐
 │ LAYER 2: PIPELINE COMPOSER (Development Core)                                                          │
 │ 20% Efficiency Gain in Dataform Dev                                                          │
 │ ┌───────────────────────────────────────────────┐  ┌─────────────────────────────────────────────────┐ │
 │ │ DTV Agent & Dataform Dev (Automates SQLX)     │  │ SQL Converter (SAP BW to BigQuery SQL)          │ │
 │ └───────────────────────────────────────────────┘  └─────────────────────────────────────────────────┘ │
 └───────────────────────────────────▲────────────────────────────────────────────────────────────────────┘
                                     │
 ┌───────────────────────────────────┴────────────────────────────────────────────────────────────────────┐
 │ LAYER 1: SEMANTIC ARCHITECT (Pre-Dev Foundation)                                                       │
 │ 95% Accuracy in Field Naming                                                                 │
 │ ┌───────────────────────────────────────────────┐  ┌─────────────────────────────────────────────────┐ │
 │ │ Lineage GPT & Naming AI (Lineage & Naming)   │  │ Data Architecture (ER Diagram Generation)       │ │
 │ └───────────────────────────────────────────────┘  └─────────────────────────────────────────────────┘ │
 └────────────────────────────────────────────────────────────────────────────────────────────────────────┘

4. End-to-End AI Agent Migration Workflow

The automated transformation pipeline executes across a structured three-phase lifecycle:

• Layer 1: Plan: The Swarovski team inputs source queries. The Analysis Agent evaluates MultiProviders/CompositeProviders (80% efficiency gain, 90% accuracy). The Planning Agent classifies SAP BW queries into Gold-layer buckets, verifies Silver readiness, constructs data models, and queries users for missing parameters (80% efficiency gain, 70% accuracy).
• Layer 2: Build: The Development Agent constructs Dataform code, verifying joins, unions, and transformation rules (90% efficiency gain, 80% accuracy). Human developers perform review aligned with SME feedback. The Validation Agent executes dual-run reconciliation between SAP source extracts and BigQuery Gold tables (90% efficiency gain, 90% accuracy).
• Layer 3: Consume: The Power BI / Looker Agent structures data models, defines relationships, and implements DAX/LookML dynamic measures (80% efficiency gain, 80% accuracy) prior to final business dashboard creation.

5. Telemetry Modeling & LookML Semantic Layer

• Standardized Schema Mapping: Normalized enterprise supply chain attributes into standardized BigQuery nested structures.
• LookML Semantic Layer: Created a centralized Looker LookML semantic model governing dynamic key figures, derived tables, and enterprise metric definitions, replacing fragmented Power BI calculations.
• Lineage Tracking: Integrated Lineage GPT metadata directly into schema registries to maintain end-to-end auditability from raw ingestion to reporting views.

6. Business Value & Thermodo RFP Resonance

• Total Cost of Ownership: 40% reduction in platform TCO achieved by eliminating legacy licensing and leveraging serverless BigQuery compute.
• Query Cost & Performance: 45% optimization in BigQuery query costs alongside a 10x acceleration in query execution performance via automated partition/clustering strategies.
• Thermodo Direct Alignment: Demonstrates CLOUDSUFI's capacity to build AI-driven data engines, automated transformations (Dataform), and centralized semantic modeling applicable to Thermodo's enterprise reporting.