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NOVRIQ LABS
PIPELINE - THE 6-STAGE ENGINE

Connect, prepare, store, and deliver data for its intended use.

A pipeline may connect source systems, extract and transform records, store data, and deliver it to reports or applications. The exact stages depend on the sources, workload, and desired outcomes.

RECOVERY DESIGN

Make successful, late, and failed runs visible.

Retry and replay behavior is designed around source retention, API limits, validation rules, and the recovery policy agreed for the system. Monitoring can report incomplete or exceptional runs to the team.

Stage-by-Stage Architecture

Examples of data handling controls that can be selected for a pipeline.

01STAGE 01

Connect

API Connections & Authentication

Connect through authentication methods supported by each source and manage credentials within the chosen environment.

Choose authentication scopes and permissions for the integration
Plan request pacing around documented source limits
Use managed secret storage where available in the chosen environment
DESIGN NOTECredential and permission failures can be surfaced for review
02STAGE 02

Extract

Incremental Extraction & Checkpoints

Collect reliable, structured delta feeds from multiple business endpoints.

Use incremental checkpoints when the source exposes suitable change markers
Handle pagination and retry behavior according to source API rules
Plan backfills around available source history and retention
DESIGN NOTEReplay depends on source history and API limits
03STAGE 03

Transform

Validation & Normalization

Apply agreed transformations, quality checks, and normalization rules to source data.

Normalize timezones, currencies, or units where required
Validate expected fields and flag schema changes for review
Use stable identifiers and idempotent operations where source data supports them
DESIGN NOTEValidation rules reflect agreed data contracts
04STAGE 04

Store

Storage & Data Organization

Store processed data in a database or warehouse selected for the application and query needs.

Choose schemas and indexes for expected access patterns
Track historical changes where business requirements call for it
Plan maintenance and storage choices for the selected platform
DESIGN NOTEStorage and performance depend on workload and platform
05STAGE 05

Analyze

Metrics & Shared Data Models

Build unified data models and agreed metric definitions.

Document definitions for selected revenue and acquisition measures
Connect dimensions across sources using agreed matching rules
DESIGN NOTEMetric definitions are agreed with stakeholders
06STAGE 06

Deliver

Reporting & Application Interfaces

Deliver data to reporting tools or application interfaces selected for the users and workload.

Deliver data to reporting tools or custom application interfaces
Plan refresh behavior around source and storage latency
Apply role and row-level access controls where supported and required
DESIGN NOTERefresh expectations account for end-to-end data latency
API Outage Handling

Backoff can reduce retry pressure during a source outage; the implementation follows the vendor's rate and retry policies.

Backfill & Replay

Checkpoints and idempotent writes can support recovery where the source retains the relevant history and exposes stable record identifiers.

Proactive Alerting

Monitoring can notify the team about selected errors and anomalies; delivery timing depends on the chosen monitoring and notification services.

Need a data pipeline or integration?

Tell us about the sources, destination, data rules, and recovery needs involved. We'll scope an architecture for your requirements.

Discuss your project