Edge Delta announced the launch of two new features: Multi-Processor Nodes and Live Capture.
These innovative features are designed to streamline the creation, management, and visualization of Telemetry Pipelines, enabling organizations to handle large, complex configurations with greater efficiency and precision.
As organizations face increasing challenges with managing vast amounts of telemetry data, Edge Delta's new Multi-Processor Nodes and Live Capture capabilities provide the tools needed to gain deeper visibility and control over data flows in real time. These features, built into the platform's Pipeline interface, empower teams to efficiently process, route, and analyze their data at scale.
The creation of multiple data processing configurations within a pipeline is often a time-consuming and complicated task, especially when managing multiple data sources and formats. Edge Delta's Multi-Processor Nodes streamline this process by allowing users to group sequential processors into a single, unified node. This reduces pipeline complexity, improves visualization, and makes it easier for teams to modify and enhance their pipelines without losing clarity.
By logically grouping processors, Multi-Processor Nodes offer significant improvements to pipeline management, enabling users to:
- Simplify pipeline visualization
- Apply customized processing requirements to specific data sources and destinations
- Standardize intermediary processing to accelerate data routing
Edge Delta's Live Capture feature takes pipeline monitoring to the next level by providing real-time visibility into how data is being transformed and processed. With Live Capture, users can closely observe the flow of data through the pipeline, including:
- A live tail of logs entering and exiting the selected node
- A detailed breakdown of which log fields are impacted by processing steps
- Real-time tracking of volume changes before and after data processing
This powerful three-panel view allows users to experiment with different processing configurations and immediately see how they impact data structure and volume.
While Multi-Processor Nodes and Live Capture are highly valuable individually, their greatest potential is realized when used together. By integrating Live Capture within the Multi-Processor Node workflow, users gain unparalleled insight into how processing steps are interacting with live data. These features enable teams to optimize their pipelines with a high level of confidence, visualizing dynamic changes in data flow as new processors are added or existing ones are adjusted.
Edge Delta's end-to-end Telemetry Pipelines are designed to optimize the collection, processing, and routing of telemetry and security data at scale. With these new features, Edge Delta is empowering organizations to manage their data more effectively, reduce operational costs, and enhance downstream monitoring and analysis.
The Latest
In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...
Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...
In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ...
Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...
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.