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 the Magic ETL Python scripting tile

Access to the Python scripting tile, as seen in the following screenshot, is a paid feature for Magic ETL. Contact Domo to have the tile activated. It enables you to run Python code as part of an inline data pipeline without the need for a Python server, as that is all handled by Domo Magic:

Figure 15.1 – Python scripting tile in Magic ETL

This is a powerful feature to handle edge cases that the standard Magic ETL doesn't cover. To get some experience in using this feature, let's walk through a data cleansing example that can be applied to any category attribute in a dataset, as follows:

  1. Click the DATA main menu icon, then click on the DataFlows icon in the left-side toolbar, then click the ETL icon in the MAGIC TRANSFORM sub-menu.
  2. Change the Add DataFlow Name value to Cleanse with Python.
  3. Click, drag and drop an Input Dataset tile using the Opportunity CH7 dataset, a Python Script tile...