Book Image

Data Democratization with Domo

By : Jeff Burtenshaw
Book Image

Data Democratization with Domo

By: Jeff Burtenshaw

Overview of this book

Domo is a power-packed business intelligence (BI) platform that empowers organizations to track, analyze, and activate data in record time at cloud scale and performance. Data Democratization with Domo begins with an overview of the Domo ecosystem. You’ll learn how to get data into the cloud with Domo data connectors and Workbench; profile datasets; use Magic ETL to transform data; work with in-memory data sculpting tools (Data Views and Beast Modes); create, edit, and link card visualizations; and create card drill paths using Domo Analyzer. Next, you’ll discover options to distribute content with real-time updates using Domo Embed and digital wallboards. As you advance, you’ll understand how to use alerts and webhooks to drive automated actions. You’ll also build and deploy a custom app to the Domo Appstore and find out how to code Python apps, use Jupyter Notebooks, and insert R custom models. Furthermore, you’ll learn how to use Auto ML to automatically evaluate dozens of models for the best fit using SageMaker and produce a predictive model as well as use Python and the Domo Command Line Interface tool to extend Domo. Finally, you’ll learn how to govern and secure the entire Domo platform. By the end of this book, you’ll have gained the skills you need to become a successful Domo master.
Table of Contents (26 chapters)
Section 1: Data Pipelines
Section 2: Presenting the Message
Section 3: Communicating to Win
Section 4: Extending
Section 5: Governing

Using ETL dataflows

ETL dataflows are the primary tools for joining or merging multiple datasets and creating data transformation pipelines. Domo datasets are the only supported inputs; the output of the dataflow is one or more datasets. So that you don't get confused, ETL is a misnomer in the sense that the sequence is extract, load, and transform, or ELT – it starts with the datasets being created by connectors. In this section, we'll walk through how to create an ETL dataflow.

Creating an ETL dataflow

The scenario we are going to walk through will use the Opportunity dataset. We want to add date dimension information and cleanse the lead source type to produce a new enhanced/cleansed dataset.

Let's start by making sure we have all the input data sources we need in the necessary datasets:

  1. Download the Opportunity.xlsx file from