A reference data architecture, from source systems to analytics.
A breakdown of common data platform components. The exact layers, security controls, and recovery behavior depend on source systems, workload, and deployment requirements.
The 5-Layer Stack
Data can move through these components in sequence, with validation and monitoring selected for the use case.
Data Sources & Ingestion Endpoints
External commerce, ad networks, product telemetry, and transactional systems.
Source of raw business facts and transaction logs.
Access scopes, credential storage, and token handling depend on the source and hosting environment.
Ingestion & Stream Processing
Stateful extraction engine with persistent cursor watermarks and rate-limit managers.
Moves new or changed records; checkpoints and recovery depend on source history and API limits.
Transport security and network controls are selected for the source and deployment context.
Data Platform & Storage Layer
High-performance relational databases, cloud warehouses, and partitioned lakes.
Reliable, durable persistence configured around analytical query profiles.
Encryption, backup, and recovery settings depend on the selected storage platform.
Transformation & Semantic Modeling
dbt-modeled transformation pipelines, referential validation, and metric definitions.
Shapes raw schemas into clean, uniform analytical models.
Validation and automated checks can surface unexpected changes before release.
Analytics & Delivery Surfaces
Interactive reporting layers, BI dashboards, and custom client interfaces.
Presents verifiable numbers from the single source of truth.
Role and row-level access controls are applied where supported and required.
Fragile Ad-Hoc Scripts vs. Novriq Labs Architecture
Why traditional data connections break silently, and how engineered pipelines eliminate reporting discrepancies.
| Architectural Dimension | Fragile Legacy Approach | Novriq Labs Standard |
|---|---|---|
| Extraction Mechanics | Repeated manual exports can become difficult to reconcile | Incremental checkpoints where the source exposes suitable change markers |
| Schema Changes | Unreviewed source changes can affect downstream transformations | Validation that surfaces unexpected changes for review |
| Metric Definitions | Metrics may be defined separately in different reports | Agreed metric definitions maintained in a shared model |
| Failure Visibility | Failures can go unnoticed without monitoring | Selected run failures and anomalies can be routed to an alerting system |
| Attribution Windows | Source restatements can change previously collected records | Backfill and reconciliation rules designed around source history and business definitions |
Need an architecture review of your existing stack?
We will review your current data pipelines, audit connection reliability, and identify where silent drift is occurring.