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)
1
Section 1: Data Pipelines
7
Section 2: Presenting the Message
12
Section 3: Communicating to Win
17
Section 4: Extending
21
Section 5: Governing

Summary

In this chapter, we saw that there are powerful features we can use to interact with cards on pages. By using collections to organize the layout, as well as interactive card clicks to create page filters, date filters, and specialized cards to create interactive filters, it is easy to get to the slice of data you desire. Filters can be saved as Filter Views for reuse and shared with other users. Context is always available by looking at the filter bar and by hovering over filtered card indicators. Recall that you need to provide consistent dataset dimension field naming when you're designing the dataset field when multiple datasets are being used on a page. This helps ensure that the applied filters won't show inconsistent information across the datasets that are being used on the page.

In the next chapter, we will go a level deeper and learn about card interactions.