Working Within Dataiku (Basics)¶
Python API. Using the internal Python API, a Data Scientist can read a Dataiku DSS dataset into a dataframe, process the dataframe with Python code, and then write back to a Dataiku DSS dataset. Examples include:
R API. Using the internal R API, a Data Scientist can read a Dataiku DSS dataset into a dataframe, process the dataframe with R code, and then write back to a Dataiku DSS dataset. Examples include:
Automating Your Work in Dataiku¶
Custom scenarios. A Data Scientist can create custom scenarios using the internal Python API.
Scoring Services. An Application Developer can query scoring services on the Dataiku API node.
Bundle and Service Package Deployment. Using the Public API, a Production Environment Manager can:
Download project bundles from a Design node,
Upload them to and manage them on an Automation node, and
Transfer API service packages from an Automation node to an API node.
Administering Dataiku Remotely¶
Using the Public API, an Administrator can:
Manage security settings, such as creating users, groups, and projects, on a Dataiku DSS instance
Manage connections from Dataiku to various data stores
Populate a project with datasets, recipes, and models according to preset configurations
In this way, you can spin up and take down Dataiku instances as they are needed on cloud infrastructures.