A true secure multi-tenant data layer were each identities data is shielded, encrypted and managed across all edge nodes with privacy by design
Through policies each edge data node is always kept up to date with all distributed data, ready to process any API call it receives at scale
High innovation by abstracting the entire data layer into a multi-tenant generic stateless microservice API that scale as containers
CloudBackend was chosen to be part of MobilityXlab, a collaboration platform where large and emerging companies with pioneering ideas drive innovation within Future Mobility together. We are very proud that CloudBackend has been chosen to MobilityXlab by several of the founding partner corporations.
A managed service that solves identity and data management; availability, synchronization, data security, and enables edge cloud computing specialized for the demanding requirements of a decentralized connected world.
Our no-code data layer APIs are not only meant to be used by containers and server code, they are safe to be directly called from mobile devices, web user interfaces, or third party software (ecosystem).
Our low-code SDK is designed to allow customer onboarding through a policy governed self-service portal while ensuring seamless network discovery of the distributed edge data.
The Edge Database Platform as a Service (Edge dbPaaS) provided by CloudBackend is a fully managed service to enable any application or hardware with edge acceleration for data harvesting or distribution at low development and operational cost.
Edge acceleration as a Service
- Empowering developers with a no-code development environment to define its distributed data backbone and backend, while still enabling any data model
- Avoiding time consuming and costly complexity of orchestration across thousands of edge nodes through policy governed data management, compliance, and geo-fencing
- Scales to billions of connected devices by using stateless containerized micro services executed and distributed across infinite number of edge nodes and end-user devices
Develop with endless scalability
Our platform manages deployment, operations, and scaling of required resources as well as handles everything from central coordination of data, issuing of identities, and the decentralization of the cloud into multi-cloud, multi-region, multi-edge, using policies. Completely abstracting the underlying infrastructure and orchestration from developers, which work with the entire Edge dbPaaS through an intelligent low-code SDK with a programming model of a virtual database — the intermediate data layer.
Industrializing data across cloud and edge
- Spin up and and configure edge data acceleration to fit your actual need at any given time and pay only for used acceleration
- No backend development (no-code) and zero API/network code in clients makes building applications and systems much cheaper and less risky
- Enabling low latency requirement use cases and innovative service creation through a low-code cross-compiling SDK for apps, containers, and cloud
Edge Native as the new default
CloudBackend handles the orchestration of data, data availability, and API access to data at multiple levels end-to-end, from the consumer all the way up to the public cloud including everything in between and along the way. Our powerful intermediate data layer shardes (slices) data to make sure that not all data is replicated across the edge and cloud, allowing policies and geo-fencing to decide how information is distributed or harvested, while leaving business decisions to govern the intelligent management of data at global scale.
An intermediate data layer and in-vehicle micro cloud specialized for the demanding requirements of connected vehicles
IoT cloud data repositories, Digital Twin platforms and analytics software can be integrated through our powerful SDK
Harvest data from IoT devices, aggregate data in factory micro clouds, federate AI models, and filter data on the way to public clouds
AI/ML applications are often placed at the edge and require a data layer as data source and transport for federating AI models, learnings and insights