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Edge Delta Announces Multi-Processor Nodes and Live Capture

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.

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Edge Delta Announces Multi-Processor Nodes and Live Capture

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

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...