Book Image

Metabase Up and Running

By : Tim Abraham
Book Image

Metabase Up and Running

By: Tim Abraham

Overview of this book

Metabase is an open source business intelligence tool that helps you use data to answer questions about your business. This book will give you a detailed introduction to using Metabase in your organization to get the most value from your data. You’ll start by installing and setting up Metabase on your local computer. You’ll then progress to handling the administration aspect of Metabase by learning how to configure and deploy Metabase, manage accounts, and execute administrative tasks such as adding users and creating permissions and metadata. Complete with examples and detailed instructions, this book shows you how to create different visualizations, charts, and dashboards to gain insights from your data. As you advance, you’ll learn how to share the results with peers in your organization and cover production-related aspects such as embedding Metabase and auditing performance. Throughout the book, you’ll explore the entire data analytics process—from connecting your data sources, visualizing data, and creating dashboards through to daily reporting. By the end of this book, you’ll be ready to implement Metabase as an integral tool in your organization.
Table of Contents (15 chapters)
1
Section 1: Installing and Deploying Metabase
4
Section 2: Setting Up Your Instance and Asking Questions of Your Data
12
Section 3: Advanced Functionality and Paid Features

Creating Questions-as-Tables using SQL

In the last section, we learned that while SQL can often be an easier option, it has a major drawback: SQL questions are hard for non-SQL users to explore. This presents a clear problem for a product such as Metabase, since Metabase aims to democratize data and allow anyone, no matter how technical, to explore it and find answers to their questions. Their solution to this is the notebook editor, but we've already seen that the notebook editor either can't answer a question, such as Items Ordered, which we learned about back in Chapter 6, Creating Questions, or is too unwieldly, as we saw in the last section with the review rate.

Unfortunately, having messy or unwieldy data in your application database is more of a rule than an exception. It's often quoted that 80% of a data scientist's job is cleaning messy data. In my experience, that sounds about right, and a lot of that cleaning is done with SQL. Remember that application...