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


In this chapter, we went through a dimensional data cleansing exercise and learned that Python can be used to call Domo APIs directly to do create, read, update, and delete (CRUD) operations on Domo datasets. We saw that there is a Python package called pydomo that makes it easy to do. Then, we used standard packages such as pandas and fuzzywuzzy to do some fancy work on cleaning up the LeadSource dimension. We even made an iterative pass after adjusting our matching criteria based on data profiles in Domo to further reduce the long-tail values of dimension values. It doesn't take much imagination to see how this process could be generalized across multiple dimensions and run on a schedule to scan new rows that have not been cleansed to improve data quality in a dramatic fashion.

In the next chapter, we will explore one of the Domo platform's machine learning (ML) capabilities.