Book Image

Datadog Cloud Monitoring Quick Start Guide

By : Thomas Kurian Theakanath
Book Image

Datadog Cloud Monitoring Quick Start Guide

By: Thomas Kurian Theakanath

Overview of this book

Datadog is an essential cloud monitoring and operational analytics tool which enables the monitoring of servers, virtual machines, containers, databases, third-party tools, and application services. IT and DevOps teams can easily leverage Datadog to monitor infrastructure and cloud services, and this book will show you how. The book starts by describing basic monitoring concepts and types of monitoring that are rolled out in a large-scale IT production engineering environment. Moving on, the book covers how standard monitoring features are implemented on the Datadog platform and how they can be rolled out in a real-world production environment. As you advance, you'll discover how Datadog is integrated with popular software components that are used to build cloud platforms. The book also provides details on how to use monitoring standards such as Java Management Extensions (JMX) and StatsD to extend the Datadog platform. Finally, you'll get to grips with monitoring fundamentals, learn how monitoring can be rolled out using Datadog proactively, and find out how to extend and customize the Datadog platform. By the end of this Datadog book, you will have gained the skills needed to monitor your cloud infrastructure and the software applications running on it using Datadog.
Table of Contents (19 chapters)
Section 1: Getting Started with Datadog
Section 2: Extending Datadog
Section 3: Advanced Monitoring


We have looked at both Datadog-supplied integrations and the options to implement integrations on your own. A Datadog environment that monitors a large-scale production environment would use a mixed bag of out-of-the-box integrations and custom checks. Though it's easy to roll out custom checks in Datadog, it is advised to look at the total cost of doing so. In this chapter, you have learned how to select the right integrations and how to configure them. Also, you learned how to do custom checks if that is warranted, in the absence of an out-of-the-box integration.

Continuing with the discussion on extending Datadog beyond the out-of-the-box features available to you, in the next chapter, we will look at how the Datadog API can be used to access Datadog features and use them for implementing custom integrations.