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 containerisedThis allows it to be adapted to both on-premise and cloud environments with minimal adaptations.
- It is also possible to deploy a shorter version Graphenus in constrained infrastructure environments.
Procurement
- Graphenus allows access multiple data sources thanks to the connectors provided by Spark and Trino.
- The integration of Kafka allows the incorporation of Graphenus in event-driven architectures, propagating information in real-time or near real-time.
Integration with other systems
- Graphenus is fully apifiedThis facilitates integration with any external system.
- All Graphenus components are decoupledThe new system will facilitate the evolution and replacement of the same.
Batch & Real Time
- Graphenus supports traditional use cases (batch) as well as those requiring Real Time.
- Business logic developed with Graphenus can be reused in both types of processing.
Infrastructure needs
- Graphenus is adaptable to both infrastructure environments limited as well as clusters formed by hundreds of servers.
- It is possible to from a reduced computational scheme and scale based on the evolving needs of 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 on containers (Docker), facilitating multiple alternatives deployment:
- On Premise
- Cloud Vendors (ARSYS, AWS, Azure, Google)
- Hybryd
- Ease of admission new storage formats (Delta Lake, Apache Iceberg...)
- Graphenus facilitates the execution of processes in the batch and streaming.
- Graphenus has at its disposal multitude of connectors with different data sources, including event ingestion using Kafka.
