domo/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.

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.

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.

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.

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.

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.