Build recipes¶
Building a recipe is the first step in preparing your data. When you start a Wrangle 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.
Wrangling requirement
To wrangle data, you must add a dataset using a configured data connection.
Availability information
The ability to perform wrangling and pushdown on datasets stored in the Data Registry is off by default. Contact your DataRobot representative or administrator for information on enabling the feature.
To wrangle Data Registry datasets, you must first add the dataset to your Use Case. Then, you can begin wrangling from the Actions menu next to the dataset. Note the following:
- This feature is only available for multi-tenant SaaS users and installations with AWS VPC or Google VPC environments.
- You can wrangle datasets up to 20GB.
Feature flag: Enable Wrangling Pushdown for Data Registry Datasets
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 tab regardless of whether or not you add operations to it. Then, each time you modify the recipe, your changes are automatically saved. Additionally, you can open saved recipes to continue making changes.
| Topic | Description |
|---|---|
| Build a recipe | Publish a recipe to push down transformations to your data source and generate an output dataset. |
| Add operations | Build a recipe to interactively prepare data for modeling without moving it from your data source. |
| Time series wrangling | Manually or automatically create a derivation plan for time series data. |
| 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. |