Netdata

Netdata is a distributed observability platform designed for real-time infrastructure monitoring and performance tracking. It functions as a high-frequency agent that collects system, container, and application metrics with per-second precision, providing both local visualization and centralized aggregation across complex, multi-cloud environments.

The platform distinguishes itself through edge-based intelligence, utilizing local machine learning models to automatically detect performance anomalies without requiring manual configuration or external query engines. Its architecture prioritizes local-first data persistence and secure metadata-only synchronization, ensuring that granular observability data remains on the host while essential system information is routed to a cloud-connected management plane. This hierarchical approach allows for horizontal scaling through parent-child node relationships, enabling unified monitoring and alerting across distributed infrastructure.

Beyond core collection and analysis, the system supports automated troubleshooting through natural language querying and intelligent metric correlation. It features a modular data acquisition engine that employs thread-per-core execution for low-latency performance, alongside isolated external processes for heterogeneous application support. The platform includes automated service discovery, diverse deployment options, and built-in diagnostic utilities to maintain visibility and connectivity across large-scale clusters.

Installation is supported through various methods including package managers, automated scripts, source compilation, and containerized orchestration.

Features

System Metrics Collection - Extracts native system-level performance data using high-efficiency threads to minimize resource overhead.

Monitoring and Observability - Delivers deep system visibility through pre-built dashboards, intelligent alerting, and automated metric correlation.

Metric and Performance Monitors - Processes and displays high-frequency numerical performance data and system health indicators.

Infrastructure Monitoring - Monitors hardware health and resource utilization across servers and cloud environments in real-time.

Distributed Observability Platforms - Unifies telemetry data from multiple nodes into a centralized architecture for scalable visibility.

Telemetry Collectors - Consolidates telemetry streams from diverse sources to support monitoring across complex, distributed environments.

Anomaly Detection Systems - Employs edge-based machine learning to automatically detect irregularities in data streams without requiring manual configuration.

Cloud Monitoring Dashboards - Aggregates local agent data into a unified cloud interface for centralized monitoring and alerting.

Agent Deployment Strategies - Simplifies large-scale distribution through support for package managers, automated scripts, and containerized orchestration.

Observability Data Isolation - Maintains data privacy by isolating sensitive metrics locally while transmitting only essential metadata to cloud services.

Thread-Per-Core Architectures - Utilizes a thread-per-core execution model to perform high-frequency data collection with minimal latency.

Cloud-Connected Management Planes - Provides a management plane to oversee distributed local agents and consolidate infrastructure insights.

Edge Anomaly Detection - Applies local machine learning models to historical metric streams to identify performance deviations at the edge.

Performance Visualization - Renders real-time performance data and system metrics through an integrated, web-based dashboard.

Metric Streaming - Manages the real-time transmission of monitoring metrics between nodes using secure API keys and connection roles.

Machine Learning Operations - Real-time performance monitoring.

DevOps and Infrastructure - AI-powered full-stack observability.

Observability - Application monitoring and observability platform.

Observability and Monitoring - Real-time performance monitoring for systems.

Miscellaneous Tools - Real-time observability tool for systems.

Desktop Applications and Tools - Distributed real-time monitoring agent for systems and apps.

Application Metrics Collection - Collects application-level telemetry using modular, language-agnostic interfaces.

Containerized Observability - Deploys monitoring agents within containerized environments to track service health and infrastructure performance.

Local-First Persistence - Persists high-resolution telemetry data directly on the host filesystem to ensure continuous availability during network outages.

Hierarchical Metric Aggregation - Forwarding observability data through structured node hierarchies allows for efficient centralization of metrics across distributed infrastructure.

Hierarchical Scaling - Organizes complex multi-cloud environments by linking parent and child nodes to centralize data retention and infrastructure-wide alerting.

Metadata-Only Synchronization - Synchronizes only essential system metadata with remote services to ensure granular data remains private and local.

Automated Rollout Managers - Facilitates consistent software updates and configuration rollouts across all distributed nodes through automated management scripts.

Service Discovery - Automates the detection of running containers and network endpoints to ensure immediate metric collection for services with custom configurations.

Automated Root Cause Analysis - Correlates system data using natural language analysis to pinpoint the underlying causes of infrastructure performance issues.

Streaming Diagnostics - Inspects live system logs across node connections to identify and report connectivity errors in real-time.

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Features

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