Cisco has officially announced the launch of Cisco Data Fabric, which happens to be a revolutionary new architecture focused on empowering organizations to harness the value of their machine data with AI.
According to certain reports, this particular innovation leverages Splunk platform to dramatically bring down the cost and complexity associated with the process of handling machine data at scale and leveraging it for AI applications, such as training custom AI models. The idea behind that is to power agentic workflows, or if not that, correlate multiple streams of both machine and business data to extract insights and drive better decisions.
More on the same would reveal how Cisco Data Fabric is purpose-built for the AI era, meaning it can allow for organizations to innovate faster, strengthen security, and achieve greater business agility. Not just that, it also activates and unifies machine data from every corner of the enterprise so to reach upon turnkey solutions that reduce cost and complexity, while simultaneously overcoming the challenges of managing distributed data at scale.
“Organizations everywhere are sitting on a gold mine of machine data that’s been too complex, cumbersome, and costly to leverage for AI, until today,” said Jeetu Patel, President and Chief Product Officer at Cisco. “From sensor readings and factory metrics to checkout system data and event updates from apps, servers, networks and more, machine data drives how businesses operate. Splunk revolutionized data and analytics for the cloud. And now, the Cisco Data Fabric is poised to do the same for AI.”
Talk about the whole value proposition on a slightly deeper level, we begin from the promise of a unified intelligent data foundation. This translates to how the new solution simplifies transformation of data across edge, cloud, and on-premises, including SecOps, ITOps, DevOps, and NetOps, into real-time, actionable insights.
Next up, there is a feature committed to cross-domain real-time search and analysis. You see, Cisco Data Fabric is well-equipped to quickly search and analyze data where it resides, federating across sources like Amazon S3, Apache Iceberg, Delta Lake (with Spark), Snowflake, and Microsoft Azure.
Alongside that, it also intelligently routes data to the most appropriate storage or analytic engine for the workload.
“Our goal is to give customers the fastest, most secure path from data to action,” said Kamal Hathi, SVP and GM, Splunk, a Cisco company. “By embedding AI across the platform and embracing open standards, we’re not just helping organizations analyze information faster—we’re enabling them to anticipate change, scale innovation without unnecessary complexity, and deliver digital services that are more resilient, adaptive, and responsive to the needs of their users.”
Another detail worth a mention is rooted in the availability of a flexible and open architecture. As a result of that, it is able to adapt to multiple environments, spanning on-premises and cloud deployments, including high compliance with open standards, plug-and-play integrations, and self-service tools to empower innovation without limitations.
Beyond that, the underlying technology will introduce a time series foundation model, which is capable of powering advanced pattern analysis and temporal reasoning on time series data. The idea here is to facilitate advanced anomaly detection, forecasting, and automated root cause analysis across the Cisco Data Fabric.
The model can also drive proactive operations, accelerate incident response, as well s turn machine data into actionable intelligence.
In essence, having unique capabilities such as Splunk Machine Data Lake should tread up a long distance to provide an AI-ready foundation for both model training and enterprise analytics. When leveraged with the Splunk AI Toolkit and Splunk Model Context Protocol Server, these innovations can even transform machine data into a fuel source for advanced AI capabilities.
“The Cisco Data Fabric addresses a critical pain point in today’s AI-driven enterprise: the challenge to quickly and securely unify vast streams of machine data for continuous resilience,” said Archana Venkatraman, senior research director, cloud data management, IDC. “By enabling a federated approach that eliminates data movement, it provides a pragmatic solution for organizations operationalizing AI at scale. Its focus on real-time search, coupled with a repository for AI-ready data, provides tangible value by reducing complexity and time to insights.”
