‍The Modern Data Collection Problem

Enterprise observability and security architectures are being crushed by the cost and complexity of collecting telemetry at scale. With thousands of VMs, bare-metal hosts, and containerized services spread across hybrid environments, most teams rely on a fragmented patchwork of Syslog agents, Fluentd/Fluent Bit nodes, Syslog daemons, other open-source agents or collectors, and proprietary vendor forwarders.

What starts as a well-intentioned data collection plan quickly turns into a maintenance nightmare:

• Configs drift across environments
• Agents need per-host updates
• Infrastructure and network costs balloon due to unfiltered, unstructured data, leaving the edge
• Insecure open-source agents and collectors are huge security risks

Observo AI Edge Collector was purpose-built to address this. It provides a unified agent framework that is centrally configured, highly scalable, and built on modern telemetry standards like OpenTelemetry (OTEL)—while delivering full fleet visibility, control, security, and performance.

Architecture: Fleet Management as a First-Class Capability

Edge Collector is tightly integrated with the Observo AI platform. Architecturally, it sits upstream of the core AI-powered Data Pipeline and handles collection, filtering, and lightweight processing at the source.

Fleet management is comprised of three elements:

1. Agents – Lightweight, OTEL-based collectors deployed at the edge
2. Fleets – Logical groupings of agents (e.g., “prod-linux-east” or “win-dev”)
3. Configurations – Declarative policies that define what to collect, how to transform it, and where to route it

This architecture enables scalable telemetry collection that can be managed declaratively, much like Kubernetes manages compute. Instead of hand-tuning syslog.conf on 10,000 endpoints, you define it once and push it everywhere.

Agent Installation: Declarative, Repeatable, Cross-Platform

Installing Observo AI’s Edge Collector is designed to be frictionless. Once a configuration is created in the UI, the platform auto-generates an installation script tailored to the platform (Linux, Windows, macOS) and associated site.

The script performs the following:

• Pulls the OTEL-based collector binary from a secure repository
• Registers the agent with the assigned configuration and fleet (if applicable)
• Starts collecting and reporting metrics within seconds (includes host metadata, version info, and heartbeat)

All agents are visible in the Fleet dashboard with metrics such as:

• Status (active, inactive)
• Host OS, version, IP, MAC address
• CPU/memory utilization
• Volume of telemetry sent
• Config version

This structure supports rapid onboarding at scale—one script, many nodes. Observo AI’s Observo AI’s Edge Collector installation script is built for fleet-wide deployment with minimal manual intervention. Whether you're managing ten nodes or ten thousand, the same lightweight script can be executed across all of them—automatically configuring each instance to collect and route data based on predefined settings. There’s no need for custom installs or node-specific tuning, which dramatically reduces setup time. Security and DevOps teams can roll out data collection across large environments in hours, not days—freeing up engineering resources and accelerating time to insight.

Configurations: Edge Logic Without the Complexity

Each configuration represents a declarative definition of:

• What data to collect (logs, metrics, events)
• Which filters to apply (regex, include/exclude patterns)
• Any lightweight transforms (basic field parsing, tag injection)
• Target destination (Observo pipeline, SIEM, or third-party tool)

Edge Collector supports the use of OTEL collector schemas (including the OTLP protocol), allowing for alignment with broader observability strategies. This enables:

• Schema normalization before data enters the pipeline
• Easy integration with standards-based platforms
• Reduction of vendor lock-in due to shared field formats

While edge agents can forward data directly to third-party systems, most users route data through the Observo AI Data Pipeline to enable richer transformations, AI-driven reduction, and multi-destination routing.

Why Filter at the Edge?

One of the biggest cost drivers in cloud-native telemetry is the ingestion of unnecessary data:

• Verbose logs from chatty apps
• Metrics never queried or visualized
• Repetitive fields that blow up payload sizes

Every unfiltered payload incurs cloud egress, processing, and storage costs. Observo AI allows teams to apply filters and exclusion rules directly at the edge, ensuring that only high-value data enters the pipeline.

Examples:

• Drop 90% of health-check logs from Kubernetes clusters
• Forward only audit events, not debug logs, from Windows hosts
• Collect metrics only during business hours to reduce noise

Centralized Configuration Management: No More Agent Drift

Legacy syslog or Fluent Bit setups require either:

• Per-host configuration (drift risk)
• A config management system (Puppet, Ansible, etc.)

With Observo AI, configuration updates are pushed from a central UI, allowing you to:

• Roll out changes instantly across thousands of agents
• Version control and audit config changes
• Deploy by fleet, platform, or site

This enables fast responses to changing requirements—whether you need to mask a sensitive field, change a destination, or tune sampling rates.

Replacing Legacy Agents and Syslog Infrastructure

Edge Collector is a direct replacement for:

• rsyslog, syslog-ng: with flexible parsing and schema alignment
• Fluent Bit / Logstash: with simpler configuration and better scale
• Heavy forwarders from legacy SIEM vendors: with significantly lower CPU footprint and no proprietary lock-in

Because it’s OTEL-based, you inherit a modern, open-source ecosystem:

• OTLP support for logs, metrics, and traces
• Integration with standards-based tools
• Easier downstream parsing and correlation

When to Use the Full Observo AI Data Pipeline

While Edge Collector can deliver filtered data directly to a third-party SIEM or observability tool, most teams pair it with the Observo Data Pipeline to:

• Route data to multiple destinations
• Perform regex-heavy parsing and transformation
• Archive full-fidelity data to a lake
• Apply Observo’s AI-driven recommendations and anomaly scoring

This makes Edge Collector a lightweight, intelligent ingestion layer—the front door to your observability pipeline.

Distributed Doesn’t Have to Mean Disconnected

Observo AI Edge Collector brings order to edge telemetry chaos. Whether you're running 1,000 nodes or 100,000, you can now deploy, manage, and optimize data collection from a single control plane—with fine-grained filtering, version control, and full operational visibility.

Want to see how it works? Check out our Observo Edge Collector product page. Contact us to schedule a demo and learn more.

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