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

Best practices

The availability of client libraries and the option to build custom integrations add a lot of flexibility to your toolbox for integrating Datadog with another application or even a batch job. However, there are certain best practices that you need to look at before starting to implement automation or customization using one or more of those options:

  • If you can choose the programming language, pick a language that is better supported and popular, such as Python for scripting and Java for enterprise applications. If the application to be integrated runs primarily runs on Microsoft Windows platforms, choosing C# would be wise.
  • Choose a client library that is officially maintained. It's a no-brainer – you need to rely on a library that will keep up with the enhancements made to the Datadog platform and REST API.
  • Plan to manage Datadog resources as code using Terraform. Ansible can help there too, but its support for Datadog is limited as of now...