DMS Architecture Diagrams
🏢 SaaS / Delivery Model
🤖 Autonomous Agents
🔮 AI Data Intelligence
📚 D&A Uplift (Scott)
🧠 Brain·Nerves·Memory
🔮 AI Data Intelligence Platform
⚡ Sales Architecture
🧠 Internal Architecture
Core Platform
Replication Engine
Agent Core (GAC)
Microservices
CDC Engine
GLDM Lineage
IP Protection
Smart Replication
📋 Roadmap
BI Studio
Execution Powertrain
Infrastructure (PNGs)
⚖️ Recon Dashboard
⚡ Horizon Power
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Gentrack AI Data Intelligence Platform — GLDM state, product plan, AI vision, revenue, customer uplift. Click any card for AI chat.
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D&A Uplift — All resources for Scott's Data Platform discussion.
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Gentrack AI Data Intelligence Platform — GLDM state, product plan, AI vision, revenue products, customer uplift. Click any card for AI chat.
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Interactive sales architecture — connectors, engine, AI, targets, cutover. Click components for details.
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Internal view — agent wiring, security layers, testing embedded, environment topology, GAC.
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Product Roadmap — Project pipeline & technical evolution. Interactive with Gantt chart.
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┌──────────────────────────── DMS EXECUTION POWERTRAIN ────────────────────────────┐ │ │ │ LANGUAGE Python 3.14 │ │ FRAMEWORK FastAPI (uvicorn, port 8765, async) │ │ DATA ENGINE Polars LazyFrames (constant memory, Rust-backed, 10-50x Pandas) │ │ DATABASE Snowflake (SOURCE read-only + LAB read-write) │ │ AI AWS Bedrock Claude 3.5 Sonnet (14 agents) │ │ COMPUTE App Runner (web) + ECS Fargate (workers, 3-50 containers) │ │ STATE DynamoDB (checkpoints, audit, API users, telemetry) │ │ STORAGE S3 (Parquet staging, exchange, IP vault, builds) │ │ CI/CD CodeBuild → ECR → App Runner (manual deploy, auto OFF) │ │ SECRETS AWS Secrets Manager (Snowflake, API keys) │ │ │ ├──────────────────────────── PIPELINE FLOW ───────────────────────────────────────┤ │ │ │ ┌─────────┐ ┌─────────┐ ┌───────────┐ ┌──────────┐ ┌────────┐ │ │ │EXTRACT │──►│ PROFILE │──►│ TRANSFORM │──►│ VALIDATE │──►│ LOAD │ │ │ │Keyset │ │Stats+AI │ │CDM 41 ent │ │Rules+VEE │ │S3→COPY │ │ │ └─────────┘ └─────────┘ └───────────┘ └──────────┘ └────────┘ │ │ │ ├──────────────────────────── ORCHESTRATION ───────────────────────────────────────┤ │ │ │ MasterAgent: THINK (decompose job) → SWARM (parallel Fargate) → REVIEW (check) │ │ Never self-approves · Human gates for mapping/PII/recon · Wave DAG (5 waves) │ │ │ ├──────────────────────────── SCALING ─────────────────────────────────────────────┤ │ │ │ <5K rows → 1 worker (in-process) │ │ 5K–100K → 3 Fargate workers │ │ 100K–1M → 5–10 workers + partitioning │ │ 1M–10M → 10–20 workers + ACCNO partitions │ │ >10M → 20–50 workers (full swarm) │ │ │ ├──────────────────────────── PROVEN SCALE ────────────────────────────────────────┤ │ │ │ Mercury: 125M rows │ Genesis: 681K CDC + 74K BCT │ Pulse: 2.34B │ PG: 217K │ │ │ ├──────────────────────────── SERVICES (Build #340) ───────────────────────────────┤ │ │ │ App Runner: DEV/UAT/DEMO/PROD (4 services) │ │ Microservices: DMS-Masker, DMS-BCT, DMS-Replication │ │ Lambdas: dms-bridge, dms-integrations, dms-ai-query │ │ Gentrack Agent Core: /gac (96 files, 869 tests) │ │ IP Protection: S3 vault + DynamoDB + Telemetry + Licensing │ │ │ └──────────────────────────────────────────────────────────────────────────────────┘
1. Current State Infrastructure
2. Data Masking Pipeline
3. Mercury Deployment Model
4. Security Layers
5. Customer Portal Boundary