domo/magic-etl

Design and configure Magic ETL dataflows with drag-and-drop tiles and scripted transformations.

Updated Apr 29, 2026

Search Tags:#scripting #etl #data-pipelines #dataflow #domo #magic-etl #json-expand

Ready to run

npx skills add https://github.com/MantisWare/BizForge --skill domo-magic-etl

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: domo/magic-etl
Source: https://github.com/MantisWare/BizForge/tree/main/library/skills/domo/magic-etl
Command: npx skills add https://github.com/MantisWare/BizForge --skill domo-magic-etl

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Magic ETL design and configuration lets teams visually build, transform, and automate data pipelines in Domo, combining multiple datasets with drag-and-drop tiles, scripting tiles (Python/R), and scheduling.

Core Features & Use Cases

Visual dataflow designer with drag-and-drop tiles for inputs, transforms, and outputs.
Scripting tiles (Python/R) for custom transformations and logic.
JSON Expand to flatten nested data structures for downstream processing.
MySQL DataFlow patterns for complex SQL-based transformations.
Scheduling options (manual, cron-based, or event-triggered runs) to automate pipelines.
Data science tiles for predictive analytics and ML-style workflows in data prep.

Quick Start

Open the Magic ETL designer, create a new dataflow, and wire an input dataset to an output dataset to start processing.

Frequently Asked Questions about domo/magic-etl

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I build data pipelines in Domo using Magic ETL?▼

To build data pipelines in Domo, Magic ETL provides a visual designer where you wire input datasets to output datasets using drag-and-drop tiles for transformations. You configure inputs, apply transforms, and set outputs to process data repeatably.

Can I use Python or R scripts for custom transformations in a Domo dataflow?▼

Yes, Domo Magic ETL supports scripting tiles for Python and R, allowing you to apply custom transformations and logic within your visual dataflow. These scripted transforms integrate directly into the drag-and-drop pipeline alongside other processing tiles.

How does JSON Expand work when flattening nested data in Domo Magic ETL?▼

JSON Expand in Domo Magic ETL flattens nested data structures into tabular formats for downstream processing. It parses complex JSON arrays and objects from your input datasets, converting them into individual rows and columns accessible by subsequent transform tiles.

What's the best way to schedule automated data pipelines in Domo?▼

The best way to schedule automated data pipelines in Domo is using Magic ETL's scheduling options, which support manual, cron-based, and event-triggered runs. You configure these settings on the dataflow to ensure governance and automate pipeline execution based on your timing needs.

Does Magic ETL support MySQL DataFlow patterns for complex SQL transformations?▼

Yes, Magic ETL supports MySQL DataFlow patterns to execute complex SQL-based transformations within your pipeline. This allows teams to combine visual drag-and-drop tiles with SQL logic to build repeatable, governed data processing workflows.

When should I use data science tiles in a Magic ETL dataflow?▼

You should use data science tiles in a Magic ETL dataflow when incorporating predictive analytics and ML-style workflows into your data preparation. These tiles allow you to apply advanced modeling logic directly during the pipeline transformation stage before output.