01
Modular Architecture
Adopt only the modules you need. Every component — from warehousing to AI — is independently deployable yet natively interoperable.
Platform overviewGENEDATA / 01
A connected platform.A new perspective.
From the first connection to the final decision, your data, models, and teams work on one governed foundation.
Shared context · Lineage · Governance
Connected
Your sources
Applications
Data platforms
Documents & events
Connected intelligenceOne control plane
Governance
Business impactEvery team, connected
Shared contextLineageGovernance
02
Single Source of Truth
One control plane manages identity, lineage, policy, and metadata across every workload, environment, and region.
03
Built to Scale
From single-team analytics to multi-tenant enterprise deployment, the platform scales horizontally without re-architecting.
Explore the stack
What runs in each layer.
Eleven modules, each belonging to exactly one layer. Open a layer to see which modules live there and what they inherit from the control plane — the same catalog, the same policy, the same lineage, whichever one you start with.
GeneFlow control plane — spans every layer
Select a layer, or use ↑ ↓ to move through the stack.
Layer 6 of 6
Presentation
Self-service dashboards, scheduled reporting, and governed external sharing — all reading from the same semantic layer, so a number on a board deck matches the number in a notebook.
Modules in this layerEnd-to-End Flow
From source system to answer, without a handoff.
Data lands, persists, transforms, and is served on one substrate. There is no export step between stages, which is where lineage normally breaks and governance normally stops.
- 01IngestData sources
- 02StoreOne engine
- 03TransformDeclarative
- 04GovernPolicy + lineage
- 05ServeApplications & analytics
GOVERNED CONTINUOUSLY BY THE GENEFLOW CONTROL PLANE
Role workbenches
Map your team to GenedataOne platform, focused for the work each person owns.
Each role gets a focused starting point without being separated into another tool. Handoffs keep their lineage, policy, approvals, and operating history.
Data Engineer
Reliable data products
Data Scientist & Actuary
Model and actuarial lifecycle
Data Analyst
Decision analytics and briefings
Data Steward
Trust, access, and knowledge operations
Executive & GM
Portfolio decisions and controlled action
The Eleven Modules
Adopt one. Adopt all eleven.
Each module stands alone and earns its place on its own merits — but because they share a catalog and a control plane, every module you add makes the others more useful rather than more complicated.
Ingestion
GeneCatalog & Knowledge
Catalog, lineage, glossary, knowledge, and impact analysis in one graph.
Storage
Cortex SQL & Warehouse
Cloud-native columnar storage tuned for analytical scans across petabyte-scale tables.
Storage
Operational Database
Low-latency operational tables sharing a planner with the warehouse — no reverse ETL.
Processing
GeneFlow Engineering
Declarative pipelines with incremental transforms and dependency-aware scheduling.
Governance
Governance & Approvals
Policy, access, and compliance defined once in the control plane and enforced everywhere.
Governance
Security & Access
Zero-trust controls, end-to-end encryption, and immutable audit across every query path.
Governance
Observability & Operations
Freshness, volume, and schema-drift signals on every table, with anomaly alerting.
Intelligence
Data Science & MLOps
Notebooks, feature stores, and model lifecycle inside the governance boundary.
Intelligence
Cortex AI & Agents
Embedded agents that monitor pipelines, explain anomalies, and draft transformations.
Presentation
Analytics & Reporting
Self-service dashboards and reporting reading from one shared semantic layer.
Presentation
Sharing & Interoperability
Governed cross-tenant sharing with lineage preserved across the boundary.
Unified vs Assembled
The integration tax you stop paying.
A best-of-breed stack is not free — it is paid for in glue code, duplicated policy, and the engineering time spent reconciling systems that disagree.
| Dimension | Assembled stack | Genedata platform |
|---|---|---|
| Governance | Re-implemented per tool; policy drifts between systems | Defined once in the control plane, enforced on every query path |
| Lineage | Stitched together from partial exports, often broken | Column-level and continuous, from ingestion through dashboard |
| Operational data | Reverse ETL round trip back into applications | Operational and analytical tables share one engine |
| Time to insight | Weeks — integration work dominates | Minutes — connect, model, and publish in one surface |
| Vendor surface | 6–10 contracts, overlapping and separately renewed | One platform, modular adoption, consistent APIs |
Deployment Models
Same platform, wherever it has to run.
The deployment target changes the perimeter, not the product. APIs, governance model, and security posture stay identical across all three.
Enterprise Cloud
Fully managed by Genedata with regional isolation and a 99.9% availability objective.
Fastest to productionPrivate Cloud
Runs inside your own AWS, Azure, or GCP account. Your keys, your network, your perimeter.
Data never leaves your tenancySovereign / Regulated
Deployed into a jurisdiction-bound region with residency guarantees and audited operator access.
Public sector and regulated finance11Modules
33 live · 84 cataloguedConnectors
99.9%Uptime objective
One graph runtimeExecution
The next step
You do not have to move everything to start.
Map the sources you already run, land one workload, and let the catalog fill in behind it. The platform runs alongside what you have rather than replacing it on day one — consolidation is the outcome, not the entry fee.
FAQ
The platform, answered.
How the platform's capability families work together, and what that changes.
How do GeneFlow, GeneCatalog, Cortex SQL, and Cortex AI work together?
They are core capability families on the Genedata platform. GeneFlow ingests, transforms, and orchestrates. GeneCatalog connects metadata, lineage, glossary, and business knowledge. Cortex SQL serves warehouse and analytical workloads. Cortex AI covers models and agents. They share one control plane, one governed graph, and one operating history.
Do I have to adopt all eleven modules?
No. Each module is independently deployable and useful on its own. Because they share a governed graph and control plane, every module you add makes the others more capable rather than creating another integration to maintain.
How is this different from assembling best-of-breed tools?
An assembled stack pays an integration tax in glue code, duplicated policy, and engineering time spent reconciling systems that disagree. Here the joins do not exist: governance is defined once and enforced on every query path, and lineage is continuous from ingestion to dashboard.
Where does the data physically live?
In open table formats in your own object storage. The platform deploys into the Genedata enterprise cloud, your own AWS, Azure, or GCP account, or a jurisdiction-bound sovereign region — with identical APIs and governance across all three.
What does a first deployment usually look like?
Most start with one workload rather than a platform migration — commonly consolidating ingestion onto GeneFlow or pointing Cortex SQL at existing tables. The estate consolidates from there, with each stage independently valuable.
Related content
Whitepaper
Architecture Overview
Topology, deployment models, identity, and integration patterns.
GuideMigration Approach
Consolidating a legacy estate without a cutover weekend.
ReferenceDeployment Models
Enterprise cloud, private cloud, and sovereign options compared.
StatusPlatform Status
Live health and incident history across every region.
Take the next step
See the whole platform in one session.
Walk an end-to-end workload — ingest, govern, query, and model — with a solutions engineer.