Build recipes¶
Building a recipe is the first step in preparing your data. When you start a wrangling session, DataRobot connects to your data source, pulls a live random sample, and performs exploratory data analysis on that sample. When you add operations to your recipe, the transformation is applied to the sample and the exploratory data insights are recalculated, allowing you to quickly iterate on and profile your data before publishing.
Note that when you wrangle a dataset in your Use Case, including re-wrangling the same dataset, DataRobot creates and saves a copy of the recipe in the Data assets tile regardless of whether or you add operations to it. Each time you modify the recipe, your changes are automatically saved. Additionally, you can open saved recipes to continue making changes.
See the associated considerations for important information about wrangling data in DataRobot.
| Topic | Description |
|---|---|
| Build a wrangling recipe | Publish a recipe to push down transformations to your data source and generate an output dataset. |
| Add wrangling operations | Build a recipe to interactively prepare data for modeling without moving it from your data source. |
| Time-aware wrangling | Manually or automatically create a derivation plan for time series data. |
| Create a SQL recipe | Use the SQL Editor to create a SQL-based recipe to pushdown transformations to your data source and generate an output dataset. |
| Associated considerations | Important additional information for working with wrangling. |
| Available connections in Workbench | A complete list of connections and which features they support. |
| Wrangling large Snowflake datasets | Tips for improving the performance of wrangling in Snowflake. |