Lakehouse / warehouse architecture
Choose storage, compute and serving patterns around workload shape, latency, governance, interoperability and cost rather than forcing every use case into one platform pattern.
Data & Analytics
Enterprise Data Platforms
Gromnii designs shared data platforms that organize, govern and serve enterprise information.
Sources
Ingest
Quality
Platform
Insight
Action
When this is useful
Use an enterprise data platform when analytics, applications and AI need shared access to information spread across many systems. The platform should define source ownership, ingestion, storage, governance and serving patterns without forcing every workload into one technology.
How information moves through the system
This reference shows one possible Enterprise Data Platforms arrangement. The actual design depends on the systems, constraints and controls involved.
01Sources
02Ingestion
03Storage
04Governance
05Serving
06Analytics & AI
What Gromnii builds
Data products
Package trusted datasets with clear owners, definitions, access rules and service expectations so they can be reused by analytics, applications and AI.
Ingestion pipelines
Move batch and streaming data from source systems with schema checks, retry logic, lineage and monitoring so failures are visible before downstream use.
Semantic & serving layers
Create stable business definitions and serving interfaces so dashboards, applications and AI use the same governed meaning instead of rebuilding logic independently.
Lineage and access
Record where data came from, how it changed and who may use it so sensitive information remains traceable across pipelines and consumption layers.
What matters in production
Ownership
Assign owners to data products, shared definitions, platform services and access decisions so changes have an accountable technical and business owner.
Access
Separate platform administration, engineering, analytical and application access so shared data remains reusable without becoming broadly exposed.
Cost
Measure storage, compute, ingestion and query cost by workload so shared-platform growth can be tied to actual use and ownership.
Platform reliability
Define service expectations, health signals, failure handling and recovery ownership for the platform components other systems depend on.
What it can improve
Reusable governed data
Publish trusted datasets with stable definitions, ownership and access rules so multiple teams can reuse them.
Faster analytics and AI delivery
Provide dependable ingestion and serving layers so new workloads start from usable data rather than rebuilding pipelines each time.
Visible platform economics
Track storage, compute, ingestion and query costs by workload so platform growth remains financially understandable.
Related solutions and industries
Discuss a Project
Describe what Enterprise Data Platforms should change, the systems it must work with and the constraints that matter.