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

Chapter 3: Storing Data

Now that you have created datasets, we will walk through how the data is stored and accessed in the Domo cloud. Domo, by design, enables an iterative and adaptive approach to data storage. The essential storage structure of a dataset is something that would look like a typical Excel spreadsheet. One advantage that this simplicity offers is that dataset schemas are derived from the data as it is ingested: when data is changed at the source, the schema is also automatically updated. That is kind of a big deal because in traditional data warehousing, it was all about rigid schema building and compliance – which, honestly, was slow and failed to deliver business value at cloud speed. So, although you can enforce highly normalized schemas in Domo, there is simply not a pressing business reason to do so. In fact, many Domo customers find that a year or so into the implementation, their Domo instance has organically accumulated more tabular data than their legacy...