Cloud and machine learning platform provider Cloudera, alongside Red Hat and Eurotech, have launched a new end-to-end open source IoT architecture.
This new platform is helpful for users in many ways. Organisations that are subscribed to proprietary IoT solutions find themselves in a circle of limited functionality, locked into a particular vendor, and are unable to scale. But with the new open source architecture, such enterprises will now be able use a modern IoT ecosystem that is scalable, secure, and technologically advanced without vendor lock-in.
Built on open industry standards, the new IoT architecture is fully integrated, and is flexible in operation. It runs on multi- or hybrid-cloud environments. This design is created to provide the foundational components that organisations need to quickly and securely roll out IoT use cases.
The architecture allows enterprises to expedite their IoT deployments while ensuring end-to-end data security and compliance. It lets them perform onboarding and managing connected devices or 'things,' to enabling protocol translation and performing analytics at the edge; from delivering real-time analytics and machine learning on IoT data to accelerating application development cycles, and enabling integration with enterprise applications, the architecture enables enterprises to accelerate their IoT deployments while ensuring end-to-end data security and compliance.
Cloudera reported a 23% rise in its total revenue during its most recent financial reports earlier this month, which it reported as $110.3 million on a year-on-year basis.
In the most recent quarter, the company introduced Cloudera Altus Data Warehouse – a modern data warehouse as-a-service, available on Microsoft Azure and AWS; Cloudera Data Warehouse – a data warehouse for self-service analytics; and Cloudera Workload XM – a new intelligent workload experience management cloud service that provides end-to-end visibility across the entire data warehouse, helping improve performance, reduce downtime and optimize utilization across the complete lifecycle of analytics workloads.
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