CloudFabrix’ data fabric weaves cloud, edge data together for easier management
CloudFabrix is tackling the data value problem in AI, announcing the commercial availability of the company’s Robotic Data Automation Fabric (RDAF), which combines observability, AIOps, and automation is now commercially available as a self-service, SaaS platform.
The RDAF edge solution will enable businesses to automate edge workload observability, edge AI model training and inference, and edge data optimization for delivery to data centers and the cloud. The service is targeted at companies needing real-time data management across verticals such as telcos, MSPs, and healthcare enterprises.
The RDAF platform is comprised of cfxCloud and cfxEdge, which run as microservices in the AWS cloud and hybrid data centers. The integration of AIOps, predictive insights and log intelligence help enterprises with data integration and data automation for digital transformation, according to the company.
“IBM Consulting and CloudFabrix participated in a joint Proof of Technology, showcasing IBM’s Shared Services Platform (IBM Liberty Platform) consisting of DevSecOps toolchains and pipelines in a multi-tenant architecture. The Proof of Technology successfully demonstrated data integration, MLOps and Predictive Analytics on streaming log data and cross-domain correlation and anomaly detection. RDAF platform can address broad Business and IT use cases,” said Raghava Venkat, partner & global offering leader, DevSecOps and AIOps, IBM Consulting.
Gartner has tapped data fabric as the top trend for infrastructure and operations leaders in 2022 (previously third in 2021).
CloudFabrix’ Robotic Data Automation Fabric is designed to manage data in the multi-cloud and multi-site environments of the edge network. There are three deployment models available in the RDAF — starter, standard (cfxCloud), and distributed. As the name suggests, the starter deployment model is suitable for pipeline deployment and validation that can be deployed on laptops. The company does not recommend using the starter deployment model for production purposes. The standard deployment is suitable for production deployments that should be deployed on a private or public cloud. For this deployment type, managed Kubernetes environments are recommended, but Docker can also be used. Finally, the distributed deployment model can be used when data needs to be processed closer to the edge locations.
A recent addition to the RDAF services, log intelligence improves the total cost of ownership and productivity by 50% for Splunk and SIEM users, according to the company. In a complex enterprise IT infrastructure, log intelligence uses the combination of artificial intelligence and machine learning models and configurable rules using data bots and pipelines. As a service between the enterprise IT logs and SIEM tools, log intelligence is designed for data reduction, data analytics and observability, data enrichment, and data replay.
CloudFabric plans to demonstrate the RDAF at events such as Cisco Live (Las Vegas) and AWS Summit (Milan).
“As we interacted with Observability and AIOps customers, it became apparent that every AI problem was first a data problem. This is what drove us to address the data problem with Robotic Data Automation Fabric. This is a very extensible platform and has use cases across Business, Analytical and Operational Systems,” said Bhaskar Krishnamsetty, chief product officer, CloudFabrix.
Article Topics
AI/ML | CloudFabrix | data management | IBM
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