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CloudFabrix Launches Data Fabric for Observability

CloudFabrix announced the launch of “Data Fabric for Observability” with dynamic Data Ingestion and Automation service (DIA) for the Cisco Observability Platform.

Cisco Observability Platform breaks operational silos to enable “experience as the new digital currency.” This platform is architected around an entity based relationship model for observable entities. The Platform stores only relevant OpenTelemetry (OTel) data, once the entities are modeled at design time, with operational personas now able to build relationships and correlations quickly at runtime.

CloudFabrix’s Data Fabric for Observability further enhances the Cisco Observability Platform’s “mean time to insights (MTTI) and remediations” exponentially by automating the creation of these entity models based on the data discovery, contextualization and modernization both at design time and run time. This eliminates the need for manual creation of the entity model making it easier for customers to bring any data into the Cisco Observability Platform.

CloudFabrix’s Data Fabric specifically solves these data challenges with its dynamic Data Ingestion and Automation (DIA) service.

- DIA service integrates with disparate data sources,

- Automates data normalization, contextualization (enrichment) and builds relationships and generates entity models at design time, using bot-based pipelines

- Transforms non-OTel data into OTel (OpenTelemetry with enrichment) and ingests only relevant telemetry corresponding to the entity models in the Cisco Observability Platform.

- Enables experimentation and visualization with the models with sample data and then ingests telemetry in run time with pipelines.

This service is now available from Cisco (partners) for customers with hybrid deployments and any kind of custom data sources and will enable rapid data ingestion in the Cisco Observability Platform for expedited business outcomes.

With this service CloudFabrix now has multiple application modules powered by the Cisco Observability Platform.

- vSphere Observability - targeted towards VM and IT admins, with VMware NSX

- Campus Analytics - targeted for Network Operators and CxO’s

- SAP Observability - targeted towards SAP Basis and IT admins

“CloudFabrix’s Data Fabric for Observability complements Cisco Observability Platform particularly for hybrid workloads and non-OpenTelemetry (OTel) compliant data sources. The ability to collect data from disparate data sources, create entity-based relationships and convert into OTel format for ingestion validates and demonstrates the extensibility and flexibility of the Cisco Observability Platform," said Carlos Pereira, Fellow & Chief Architect, Cisco.

“With the dynamic Data Ingestion and Automation service, customers can now observe their hybrid applications and workloads and create a roadmap for OpenTelemetry adoption, using the Cisco Observability Platform,” said Raju Datla, CEO at CloudFabrix. “This service significantly accelerates and cuts down data ingestion and time to insights,” he further added.

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CloudFabrix Launches Data Fabric for Observability

CloudFabrix announced the launch of “Data Fabric for Observability” with dynamic Data Ingestion and Automation service (DIA) for the Cisco Observability Platform.

Cisco Observability Platform breaks operational silos to enable “experience as the new digital currency.” This platform is architected around an entity based relationship model for observable entities. The Platform stores only relevant OpenTelemetry (OTel) data, once the entities are modeled at design time, with operational personas now able to build relationships and correlations quickly at runtime.

CloudFabrix’s Data Fabric for Observability further enhances the Cisco Observability Platform’s “mean time to insights (MTTI) and remediations” exponentially by automating the creation of these entity models based on the data discovery, contextualization and modernization both at design time and run time. This eliminates the need for manual creation of the entity model making it easier for customers to bring any data into the Cisco Observability Platform.

CloudFabrix’s Data Fabric specifically solves these data challenges with its dynamic Data Ingestion and Automation (DIA) service.

- DIA service integrates with disparate data sources,

- Automates data normalization, contextualization (enrichment) and builds relationships and generates entity models at design time, using bot-based pipelines

- Transforms non-OTel data into OTel (OpenTelemetry with enrichment) and ingests only relevant telemetry corresponding to the entity models in the Cisco Observability Platform.

- Enables experimentation and visualization with the models with sample data and then ingests telemetry in run time with pipelines.

This service is now available from Cisco (partners) for customers with hybrid deployments and any kind of custom data sources and will enable rapid data ingestion in the Cisco Observability Platform for expedited business outcomes.

With this service CloudFabrix now has multiple application modules powered by the Cisco Observability Platform.

- vSphere Observability - targeted towards VM and IT admins, with VMware NSX

- Campus Analytics - targeted for Network Operators and CxO’s

- SAP Observability - targeted towards SAP Basis and IT admins

“CloudFabrix’s Data Fabric for Observability complements Cisco Observability Platform particularly for hybrid workloads and non-OpenTelemetry (OTel) compliant data sources. The ability to collect data from disparate data sources, create entity-based relationships and convert into OTel format for ingestion validates and demonstrates the extensibility and flexibility of the Cisco Observability Platform," said Carlos Pereira, Fellow & Chief Architect, Cisco.

“With the dynamic Data Ingestion and Automation service, customers can now observe their hybrid applications and workloads and create a roadmap for OpenTelemetry adoption, using the Cisco Observability Platform,” said Raju Datla, CEO at CloudFabrix. “This service significantly accelerates and cuts down data ingestion and time to insights,” he further added.

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Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

The gap is widening between what teams spend on observability tools and the value they receive amid surging data volumes and budget pressures, according to The Breaking Point for Observability Leaders, a report from Imply ...

Seamless shopping is a basic demand of today's boundaryless consumer — one with little patience for friction, limited tolerance for disconnected experiences and minimal hesitation in switching brands. Customers expect intuitive, highly personalized experiences and the ability to move effortlessly across physical and digital channels within the same journey. Failure to deliver can cost dearly ...

If your best engineers spend their days sorting tickets and resetting access, you are wasting talent. New global data shows that employees in the IT sector rank among the least motivated across industries. They're under a lot of pressure from many angles. Pressure to upskill and uncertainty around what agentic AI means for job security is creating anxiety. Meanwhile, these roles often function like an on-call job and require many repetitive tasks ...