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 5: Sculpting Data In-Memory

While working with data, sometimes, for performance or convenience purposes, we must perform operations on the data that are not persistently stored to disk. For performance, an example is combining billions of rows of transactions with millions of rows of customer data. For convenience, an example might be a simple code mapping lookup or applying business logic. These fine-tuning adjustments are enabled by tools that work on in-memory caches and query technology. In Domo's case, this is Adrenaline, which is a queryable in-memory cache. These in-memory transforms are orders of magnitude faster than persistent transforms via ETL and when fast is what you are after, these tools are the right choice. Occasionally, the use of Adrenaline dataflows becomes critical in cases when the rows of data being processed reaches billions of rows, joins are required, and materialization is not practical. If you are familiar with creating non-materialized views...