¶ Datadog
¶ Operating Systems
Datadog is one of those platforms that you don’t fully appreciate until you’ve lived with it for a while. At first, it can look like a dashboard-filled monitoring tool, a place to watch CPU usage or track application latency. But once you begin using it in real environments—real servers, real containers, real distributed systems—you realize it has a way of becoming a kind of nervous system for your infrastructure. It connects pieces that used to feel separate. It reveals patterns that were previously invisible. It makes the behavior of machines feel almost conversational. And when you're responsible for keeping systems healthy, that shift in perspective changes everything.
The purpose of this course is to guide you through that shift. Across a hundred articles, we’ll explore Datadog as more than a collection of features. We’ll treat it as a lens—a tool that helps you understand how operating systems behave under pressure, how applications interact with the underlying machine, and how entire ecosystems pulse when requests flow, resources fluctuate, and workloads collide with real-world conditions. Datadog isn’t just a monitoring platform. It’s a way of thinking about systems.
This introduction is meant to set the tone for that journey. We’ll start not with dashboards or metrics or integrations, but with the idea that modern computing is messy, distributed, fast-moving, and unforgiving when something goes wrong. Servers crash. Memory leaks grow silently. File descriptors hit their limits. Network latency erupts in bursts. Disks wear down. Containers get rescheduled at inconvenient moments. And everything is connected to everything else, usually more tightly than you expect. Datadog steps into that reality not as a “nice to have” tool but as a companion—something that lets you see your systems in a way that makes sense.
To understand Datadog, it helps to remember how operators used to monitor systems. Many people relied on manual log checks, custom scripts, ad hoc shell tools, or scattered services that offered a partial view of resource usage. It was common to have one tool watching CPU, another watching memory, another alerting on logs, and yet another graphing network throughput. The experience was fractured, and you often discovered problems only after they caused visible damage. Datadog emerged from that frustration. Instead of scattering monitoring responsibilities across dozens of tools, it brings everything under one roof: metrics, logs, traces, events, uptime checks, container behavior, network visibility, security signals, and more. It doesn’t just collect data; it ties it together so you can understand the story behind the signals.
That unification is one of the reasons Datadog fits naturally into discussions about operating systems. At its core, an OS is about coordination and resource management. It decides who gets CPU time, where memory gets allocated, how disk operations get scheduled, how network connections flow, and how processes survive or fail. When Datadog collects metrics—whether that’s CPU steal time on a virtual machine, page faults on a physical server, I/O wait on a database node, or TCP retransmits on a containerized service—it’s giving you direct insight into how the underlying operating system is handling the workload you’ve thrown at it. When you learn Datadog deeply, you inevitably end up learning how operating systems really behave.
But Datadog’s strength isn’t just in its ability to collect. It’s in the way it helps you think. If you’ve ever been on call during a chaotic incident, you know how quickly your brain tries to connect dots: “Is it the database?” “Is it the app?” “Did we deploy something?” “Is the filesystem full?” “Is the network having a moment?” Datadog helps shape that reasoning. Because it correlates logs with traces, events with metrics, and spikes with patterns in other parts of the system, it allows you to zoom out when necessary and zoom in when required. It reduces the guesswork. It gives you the confidence to say not only what is happening but why.
Throughout this course, you’ll move from the foundations of monitoring to the more subtle aspects of observability: how patterns emerge, how anomalies surface, how you can use the platform not merely to detect issues but to anticipate them. You’ll explore metrics like load average, CPU saturation, inode usage, container restarts, garbage collection cycles, JVM heap pressure, kernel scheduler activity, and dozens of OS-level signals that tell a story about system health. And you’ll learn how Datadog helps you interpret those signals in a way that makes sense even when the system is complex.
One of the most rewarding parts of using Datadog is how it changes your relationship with logs. Logs used to be something you hunted through, grepping line by line, hoping to catch the root cause somewhere in a sea of text. Datadog reframes logs as structured, queryable data—signals that can be filtered, grouped, aggregated, and correlated across services. Suddenly, logs aren’t just text; they’re part of the system’s heartbeat. You’ll see how logs become more powerful when they exist alongside traces and metrics. They no longer tell isolated stories. They complete the picture.
Speaking of traces, Datadog’s tracing capabilities give you something that used to be almost impossible: the ability to observe distributed systems as if you were watching requests whisper their way through your architecture. You’ll see how a single API call hops across services, containers, databases, caches, queues, and networks. You’ll watch latency accumulate layer by layer. You’ll see bottlenecks appear clearly where previously they were invisible. Tracing turns the abstract idea of “the system is slow” into a concrete understanding of which part is slow, why it’s slow, and how system resources relate to that slowness.
Throughout the course, you’ll come to appreciate how all these signals—logs, metrics, traces, and events—compress into a simple truth: systems are living organisms. They breathe in requests, process workloads, handle spikes, recover from errors, and adapt to the environment. Datadog is the lens that lets you see that behavior with clarity. It turns the invisible into the understandable.
Another theme that will appear over and over is the relationship between Datadog and automation. Observability is useful on its own, but it becomes transformative when it informs automated decisions: scaling rules, deployment gates, anomaly detection, health checks, performance budgets, or automated rollbacks. With Datadog’s alerting engine, you won't just react to problems; you’ll learn to shape proactive patterns, define healthy behavior, and enforce guardrails that keep systems stable. You’ll see how something as simple as an alert on disk usage can evolve into a series of automated actions that preserve uptime without human intervention. Datadog isn’t merely a tool for observing systems—it can guide them.
As you progress farther into the course, you’ll also explore the cultural side of observability. Datadog doesn’t exist in a vacuum. Teams that use it well build habits around shared dashboards, thoughtful alerting policies, blameless postmortems, and communication patterns that soften the chaos of incidents. You’ll learn not just how to interpret the data but how to help teams communicate through it. Observability, at its best, isn’t just a technical practice—it’s a collaborative one.
You’ll also discover how Datadog fits into the modern world of cloud-native environments. Containers, microservices, serverless functions, clusters, orchestrators, sidecars—it’s a complex ecosystem. Operating systems aren’t just running on bare metal anymore. They’re abstracted behind layers of virtualization, distributed schedulers, and container runtimes. Datadog helps peel back those layers. It lets you see the underlying OS signals of a container running in a pod running on a node running in a cluster running on a virtual machine running on cloud infrastructure. It doesn’t just give you visibility; it gives you continuity across layers that would otherwise feel disconnected.
Through the course’s later stages, you’ll understand how Datadog becomes a source of truth for capacity planning, performance tuning, and architectural decision-making. You’ll learn to interpret long-term trends in system usage. You’ll identify patterns of load that suggest new scaling strategies. You’ll learn how to measure the real impact of code changes. And you’ll develop the habit of making architecture decisions based on data rather than intuition alone.
By the end of the hundred articles, you won’t just be capable of using Datadog. You’ll be fluent in it. You’ll look at metrics and instinctively know what the operating system is trying to tell you. You’ll feel comfortable building dashboards that speak plainly. You’ll craft alerts that matter. You’ll tune systems with confidence. And you’ll know how to use Datadog not as a collection of features but as an extension of your ability to understand systems.
This introduction is your first step into that journey. Datadog has a way of deepening your relationship with the machines you work with. It brings clarity, coherence, and a sense of calm to a world that often feels chaotic. If you’re ready to approach operating systems with sharper intuition, clearer visibility, and a stronger grasp of how all the pieces fit together, then you’re exactly where you should be.
Let’s begin.
¶ Datadog
¶ Beginner Level
1. Introduction to Datadog and Monitoring
2. Understanding the Role of Operating Systems in Monitoring
3. Setting Up Your Datadog Account
4. Installing the Datadog Agent on Linux
5. Installing the Datadog Agent on Windows
6. Installing the Datadog Agent on macOS
7. Overview of Datadog’s Key Features
8. Navigating the Datadog Dashboard
9. Introduction to Datadog Metrics
10. Collecting System Metrics from Linux
11. Collecting System Metrics from Windows
12. Collecting System Metrics from macOS
13. Understanding Datadog’s Default Dashboards
14. Configuring Datadog Agent Logs
15. Monitoring CPU Usage with Datadog
16. Monitoring Memory Usage with Datadog
17. Monitoring Disk I/O with Datadog
18. Monitoring Network Traffic with Datadog
19. Setting Up Alerts for System Metrics
20. Introduction to Datadog Tags and Their Importance
¶ Intermediate Level
21. Deep Dive into Datadog Agent Configuration
22. Configuring Datadog Agent on Multiple Operating Systems
23. Monitoring System Processes with Datadog
24. Tracking System Logs with Datadog Log Management
25. Integrating Datadog with System Logs (Syslog, Journald, etc.)
26. Monitoring Kernel Metrics on Linux
27. Monitoring Windows Services with Datadog
28. Monitoring macOS Background Processes with Datadog
29. Using Datadog to Monitor File Systems
30. Monitoring System Uptime with Datadog
31. Setting Up Custom Metrics for Operating Systems
32. Using Datadog to Monitor System Resource Limits
33. Monitoring Swap Usage with Datadog
34. Tracking System Temperature and Hardware Metrics
35. Monitoring System Boot Times with Datadog
36. Using Datadog to Monitor System Dependencies
37. Configuring Datadog for Multi-OS Environments
38. Monitoring System Security with Datadog
39. Tracking User Activity on Operating Systems
40. Monitoring System Updates and Patches
41. Using Datadog to Monitor System Performance Baselines
42. Configuring Datadog for High Availability Systems
43. Monitoring System Crashes and Reboots
44. Tracking System Errors and Exceptions
45. Using Datadog to Monitor System Threads and Handles
46. Monitoring System Interrupts and Context Switches
47. Configuring Datadog for Real-Time System Monitoring
48. Using Datadog to Monitor System Load Averages
49. Monitoring System Clock Synchronization (NTP)
50. Integrating Datadog with System Monitoring Tools (e.g., Nagios, Zabbix)
¶ Advanced Level
51. Advanced Datadog Agent Configuration for Operating Systems
52. Monitoring Custom Kernel Modules with Datadog
53. Tracking System Calls with Datadog
54. Monitoring System Resource Contention with Datadog
55. Using Datadog to Monitor System Bottlenecks
56. Configuring Datadog for Distributed Systems
57. Monitoring System Performance in Virtualized Environments
58. Using Datadog to Monitor Containerized Operating Systems
59. Monitoring System Performance in Cloud Environments
60. Configuring Datadog for Hybrid Cloud Systems
61. Using Datadog to Monitor Bare-Metal Servers
62. Monitoring System Performance in Edge Computing Environments
63. Tracking System Performance in Real-Time with Datadog
64. Using Datadog to Monitor System Performance Across Regions
65. Configuring Datadog for Multi-Tenant Systems
66. Monitoring System Performance in High-Frequency Trading Systems
67. Using Datadog to Monitor System Performance in Gaming
68. Configuring Datadog for IoT Devices and Operating Systems
69. Monitoring System Performance in Real-Time Streaming Systems
70. Using Datadog to Monitor System Performance in AI/ML Workloads
71. Configuring Datadog for High-Performance Computing (HPC) Systems
72. Monitoring System Performance in Blockchain Networks
73. Using Datadog to Monitor System Performance in Financial Systems
74. Configuring Datadog for Government and Compliance Systems
75. Monitoring System Performance in Healthcare Systems
76. Using Datadog to Monitor System Performance in Retail Systems
77. Configuring Datadog for Automotive and Embedded Systems
78. Monitoring System Performance in Aerospace Systems
79. Using Datadog to Monitor System Performance in Telecommunications
80. Configuring Datadog for Industrial Control Systems (ICS)
81. Monitoring System Performance in Smart Cities
82. Using Datadog to Monitor System Performance in Energy Grids
83. Configuring Datadog for Military and Defense Systems
84. Monitoring System Performance in Space Exploration Systems
85. Using Datadog to Monitor System Performance in Autonomous Vehicles
86. Configuring Datadog for Quantum Computing Systems
87. Monitoring System Performance in Augmented Reality (AR) Systems
88. Using Datadog to Monitor System Performance in Virtual Reality (VR) Systems
89. Configuring Datadog for Mixed Reality (MR) Systems
90. Monitoring System Performance in Robotics Systems
91. Using Datadog to Monitor System Performance in Drones
92. Configuring Datadog for Wearable Devices
93. Monitoring System Performance in Smart Homes
94. Using Datadog to Monitor System Performance in Smart Factories
95. Configuring Datadog for Smart Agriculture Systems
96. Monitoring System Performance in Environmental Monitoring Systems
97. Using Datadog to Monitor System Performance in Disaster Recovery Systems
98. Configuring Datadog for Emergency Response Systems
99. Monitoring System Performance in Critical Infrastructure Systems
100. Building a Real-World Enterprise Monitoring Solution with Datadog