In order to complete the majority of the exercises in this book, you will need to download and install Docker along with Docker Compose. For the examples in the book, we will be downloading and installing other software and datasets, including Grafana and Loki, so you will occasionally need an internet connection. You can download and install each software package independently, but our tutorial instructions are designed to work with Docker. We do that so that all software dependencies and network management can be encapsulated within the Docker container paradigm.

We will run a fair amount of software from the command line, so you should be comfortable with typing commands into a shell, such as Bash or Windows PowerShell. To access the contents of the book’s GitHub repository, you will either need Git or an unzip application.

Having an interest in science in general and data science, in particular, will go a long way toward making this book interesting and useful. It would also be helpful to have some programming experience with a scripting language such as Python, but since all the code is included, you can run it directly from a clone of the book’s GitHub repository. Some familiarity with relational databases will help you understand some of the terminology and concepts behind time-series databases.

Grafana is an application under constant development and revision, and as such, the depictions, descriptions, and illustrations in this book represent a snapshot in time and are current at the time of writing. By the time you read this book, features may have been added, altered, or deleted outside of our control. However, we believe any deviations from the book should be easily accommodated with only minor adjustments.

It might also be helpful to use an IDE application such as Microsoft Visual Studio Code, or JetBrains PyCharm.

In order to follow along with the exercises in Chapter 16, Authenticating Grafana Logins Using LDAP or OAuth 2 Providers, you will need accounts with GitHub, Google, and Okta. To follow the exercises in Chapter 17, Cloud Monitoring AWS, Azure, and GCP, you will need to create an account with AWS, GCP, and Microsoft Azure.

The examples and software in this book have not been validated for security reasons. They require an external internet connection and leverage open source software under a variety of licenses, so if you intend to use any of this software within a security-conscious computing environment (such as in an education or corporate environment), it is highly recommended that you consult your local IT professionals in advance.

I hope to show with the examples in this book how easy it is to build simple data visualization pipelines with Grafana and today’s open source tools. I also hope this book will inspire and empower you to seek out your own datasets to acquire, analyze, and visualize. Best of luck!

If you are using the digital version of this book, we advise you to type the code in yourself or access the code via the GitHub repository (link available in the next section). Doing so will help you avoid any potential errors related to the copying and pasting of code. Each chapter folder includes dashboards, docker-compose.yml files, and a Makefile to help out when running some of the command-line tools.