Overview
Prizm is designed for flexible enterprise deployment — supporting fully managed cloud, self-hosted on-premises, and hybrid configurations. The containerized architecture ensures consistent behavior across all deployment models.Deployment Models
Cloud (SaaS)
Fully managed by DQLabs. Fastest time-to-value with automatic updates and scaling.
On-Premises
Self-hosted in your own data center or private cloud. Full control over data residency.
Hybrid
Control plane in cloud, data agents on-premises. Best of both worlds.
Architecture Principles
Prizm’s deployment architecture is built on:- Containerization — All components run in Docker/Kubernetes containers
- Horizontal Scalability — Scale agent workers independently based on data volume
- Open APIs — GraphQL and REST APIs for custom integrations and extensions
- Secure by Default — TLS 1.3 everywhere, AES-256 at rest, zero-trust networking
Deployment Checklist
Choose Deployment Model
Decide between SaaS, on-premises, or hybrid based on data residency requirements and operational preferences.
Provision Infrastructure
Allocate compute, storage, and networking resources per the infrastructure sizing guide.
Configure Identity Provider
Set up SSO integration (SAML 2.0, OAuth 2.0, or LDAP) for user authentication.
Connect Data Sources
Add your first data connections — typically your primary data warehouse and a pipeline tool.
Run Initial Discovery
Execute the first asset discovery run to populate the catalog. This typically takes 15–60 minutes depending on data volume.
Customer Onboarding
DQLabs provides a structured onboarding program for new Prizm deployments:Onboarding Phases
| Phase | Duration | Focus |
|---|---|---|
| Kickoff | Week 1 | Architecture review, deployment planning, credential preparation |
| Foundation | Weeks 2–3 | Source connections, initial discovery, user setup |
| Activation | Weeks 4–5 | Profiling configuration, quality metric setup, alerting |
| Expansion | Weeks 6–8 | AI features enablement, governance workflows, additional sources |
| Steady State | Ongoing | Review cadence, metric tuning, new source onboarding |
Contact your DQLabs Customer Success Manager for a tailored onboarding plan specific to your deployment size and data ecosystem.
Infrastructure Requirements
Minimum Requirements (Small Deployment)
| Component | Specification |
|---|---|
| Control Plane | 4 vCPU, 16 GB RAM |
| Agent Workers | 2 vCPU, 8 GB RAM per worker |
| Database (MetaStore) | PostgreSQL 14+, 100 GB SSD |
| Search Index | Elasticsearch / OpenSearch 8+, 50 GB |
| Object Storage | S3 / Azure Blob / GCS — 100 GB |
Recommended (Production)
| Component | Specification |
|---|---|
| Control Plane | 8 vCPU, 32 GB RAM (HA pair) |
| Agent Workers | Auto-scaling pool, 2–20 workers |
| Database (MetaStore) | Managed PostgreSQL, Multi-AZ, 500 GB |
| Search Index | Managed OpenSearch, 3-node cluster |
| Object Storage | Unlimited (pay-as-you-go) |
| Kubernetes | EKS / AKS / GKE, 1.28+ |