Overview
Prizm features a cloud-native, containerized architecture that enables:- Seamless integration with existing data ecosystems
- Horizontal scalability to handle enterprise-level data volumes
- Deployment flexibility — cloud, on-premises, or hybrid
- Open APIs for custom extensions and integrations
High-Level Architecture
Core Architectural Components
AI-Native Control Plane
The control plane is the brain of the Prizm platform:| Component | Responsibility |
|---|---|
| Agent Orchestrator | Plans, routes, and coordinates agent tasks with memory of prior context |
| Event Bus | Decoupled message passing between agents and platform services |
| Prioritized Task Queue | Risk-based scheduling ensures critical assets are always processed first |
| Policy Engine | Enforces SLAs, guardrails, RBAC, and ABAC rules across all operations |
Knowledge & State Store
Prizm maintains a rich, interconnected knowledge base:| Store | Contents |
|---|---|
| Unified Catalog | All assets — tables, models, pipelines, dashboards — with owners and tags |
| Semantic Layer | Business terms, KPIs, glossaries, and domain context |
| Knowledge Graph | Relationships, lineage, and dependency maps |
| Vector Store | Runbooks, incident histories, and documentation for RAG-based AI |
| Operational Log | Single unified observability table across all agents |
Product Surface
- Prizm APIs — GraphQL and REST APIs for programmatic access
- Prizm UI — Full-featured web interface for catalog, metrics, issues, and governance
- Alerting & Integrations — Slack, Email, Jira, PagerDuty, ServiceNow
Sub-System Documentation
Data Model
Core entity relationships and schema design.
Metrics
Metric types, execution engine, and scoring.
Score Entity
Quality score schema, attributes, and relationships.
Scheduling
CRON, event-based, and intelligent schedule management.
Notifications
Alerting channels, routing, and notification policies.
Infrastructure
Deployment topology and infrastructure requirements.