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

Collecting logs

The first step in any log management application is to collect the logs in a common storage repository for analyzing them later and archiving them for the records. That effort involves shipping the log files from machines and services where they are available to the common storage repository.

The following diagram provides the workflow of collecting and processing the logs and rendering the aggregated information to end users. The aggregated information could be published as metrics, which could be used for setting up monitors. That is the same as using metrics to set up monitors in a conventional monitoring application:

Figure 13.1 – Log management workflow

In a modern production infrastructure, the logs could be generated by a variety of sources, and typical sources include the following:

  • Public cloud services: Public cloud services such as AWS S3 and RDS are very popular, especially if the production infrastructure is...