Pipelines built for all sources simultaneously.
None in production after six months. Priority was never established.
Aurora Forge
Data Engineering
Data Engineering
A modern data platform — warehouse or lakehouse — with reliable pipelines, governed access and the foundation laid for analytics, reporting and AI.
Common failure patterns
Governance deferred.
Data lake becomes a data swamp with no access control and no lineage.
Platform chosen before workload analysis.
Wrong architecture for the actual query patterns. Expensive retrofit.
Business problem
Operational data sits trapped in source systems. Reports are stale, dashboards conflict on the same numbers, and AI and analytics initiatives stall waiting for clean, governed inputs.
Aurora’s operating approach
Our data engineers design the architecture, build pipelines and configure governance. Our data architects define the target state. Our integration engineers connect source systems. Platform-accredited delivery partners (Snowflake, Databricks, BigQuery teams) contribute where the engagement requires vendor-specific accreditation. ETL/ELT tooling and governance platform partners join where required by scope.
Expected outcomes
A modern data platform — warehouse or lakehouse — with reliable pipelines, governed access and the foundation laid for analytics, reporting and AI workloads.
Operational deliverables
- One number, one source — the platform people actually trust.
- Governed access, with audit trail and lineage.
- A foundation that analytics, reporting and AI workloads can build on without re-plumbing.
Engagement tiers
Assessment
Data Platform Assessment — sources, target architecture, governance baseline.
Flat-fee diagnostic
Build
Pipeline build in priority order on warehouse or lakehouse target. 3–6 months.
Engagement-scoped after assessment
Platform
Full modern data platform with governance, lineage and analytics/AI enablement.
Engagement-scoped
Typical engagement lifecycle
Phase 01
Audit
Data audit and source-system mapping.
Phase 02
Architect
Target architecture and platform selection.
Phase 03
Build
Pipeline build in priority order.
Phase 04
Govern
Governance setup and internal team enablement.
Delivery partners
Aurora leads architecture, governance and pipeline build. Snowflake / Databricks delivery partners, named per engagement, contribute platform engineering depth under Aurora's program management.
DATA
Snowflake / Databricks Partner
Specialist delivery on modern lakehouse and warehouse platforms, named per engagement.
Named per engagement
HYPER
SCALER
SCALER
AWS / Azure / GCP Delivery Partner
Specialist delivery teams across AWS, Azure and GCP, named per engagement.
Named per engagement
Frequently asked questions
Warehouse or lakehouse?
Engagement-specific. The Assessment determines which fits your data shape and use cases.
Do you migrate from legacy warehouses?
Yes — incremental migration with parallel running is the standard pattern; rip-and-replace only when justified.
Next step
Data Platform Assessment
One- to two-week assessment of your data estate. Output: a written target architecture with pipeline priorities and a governance baseline.