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

There are certain patterns of best practices in log management. Let's see how they can be rolled out using related Datadog features:

  • Plan to collect as many logs as possible. It is better to stop collecting some of the logs later if they are found to be not useful.
  • Where possible, especially with the application logs that you will have control over in terms of formatting, make the format of logs parsing friendly.
  • Consider generating new logs for the purpose of generating metrics out of such logs. Such efforts have been found to be very useful in generating data for reporting.
  • Make sure that sensitive information in logs is redacted before allowing Datadog to collect.
  • Implement the redaction of sensitive information from the logs instead of filtering out log entries. However, filter out log entries that might not be useful, so the volume of logs handled by Datadog will be minimal.
  • Create a library of searches and publish it for general...