Graphenus follows a cash strategy white, transparency and flexibility to adapt to the different needs of the companies: from the component replacement until deployment in different types of environments.
Deployment models
Graphenus is containerised, allowing it to be adapted to both on-premise and cloud environments with minimal customisation.
It is also possible to deploy a reduced version of Graphenus in limited infrastructure environments.
Procurement
Graphenus allows access to multiple data sources thanks to the connectors provided by Spark and Trino.
The integration of Kafka allows the incorporation of Graphenus into event-driven architectures, propagating information in real-time or near real-time.
Integration with other systems
Graphenus is fully unified, which facilitates integration with any external system.
All Graphenus components are decoupled, facilitating their evolution and replacement.
Batch & Real Time
Graphenus supports traditional (batch) use cases as well as those requiring Real Time.
Business logic developed with Graphenus can be reused in both types of processing.
Infrastructure needs
Graphenus adapts to both limited infrastructure environments and clusters consisting of hundreds of servers.
It is possible to start from a reduced computational scheme and scale up based on the evolving needs of the companies.
Multiple deployment options and plug&play mechanisms to adapt the platform to the specific casuistry of each company.
Graphenus is fully adaptable to different infrastructure scenarios and different types of use cases:
- Graphenus runs in containers (Docker), facilitating multiple deployment alternatives:
- On Premise
- Cloud Vendors (ARSYS, AWS, Azure, Google)
- Hybryd
- Ease of support for new storage formats (Delta Lake, Apache Iceberg...)
- Graphenus facilitates the execution of processes in batch and streaming mode.
- Graphenus has a multitude of connectors to different data sources, including event ingestion using Kafka®.
