Datalake - Summary & Workflow

As a summary, in this first part of the documentation related to the Datalake, we discovered how to:

• Upload or import Data to a Datalake from a local drive or from a Cloud-based bucket
• Visualize Data, its Properties, and its fixed or Custom Metadata
• Structure Data using DataTag
• Link several versions of the same Data together with Projections
• Navigate and pinpoint relevant Data in a large Datalake using the Query Language, Charts, or Embeddings exploration
• Automate tasks on your Data with Processings
• Select a subset of Data to create a DatasetVersion

All those actions can also be done programmatically using the Python SDK.