- Entirely new here?
Understand the Value Proposition of Dataiku DSS.
Dataiku Data Science Studio Concepts¶
The Lab and the Flow¶
- Draft work in the Lab
When beginning a data project, you should start by drafting your work in the Lab environment
the Visual Analysis tool lets you draft data preparation steps, create charts, and build machine learning models
the code notebooks let you explore your data interactively.
- Build Data Pipelines in the Flow
The Flow is an end-to-end visualization of your data preparation steps from source to final data products.
- Sampled vs. Complete Data
When working on a prepare recipe or in an analysis, you get live visual feedback for all the preparation steps that you add. This couldn’t happen if you were previewing whole datasets (big data just doesn’t fit in DSS’s memory), so you are simply looking at a sample.
Keeping Everything Up-to-date in the Flow¶
- Rebuilding Datasets
When you edit an existing recipe or when your data is updated, you will need to rebuild your downstream datasets to update their contents.
- Deploy Flows in Production
Dataiku DSS greatly facilitates all automation and monitoring tasks, so make sure you understand all production features related to Flow automation.
Dataiku DSS is all about making teams more efficient, so don’t forget to promote your work to others!