End-to-end data science scenario
A typical data analytics system for data science in Fabric would consist of the components and layers shown in Figure 6.1:
Figure 6.1 – Reference architecture for data science in Fabric
Let’s review these components in detail:
- Data sources: To ingest data into the lakehouse either from Azure data services or from other cloud platforms or on-premise sources, Fabric provides native or built-in ready-to-use connectors to make use of it, which makes building a data ingestion flow quick and easy. In Fabric, you might also use the data from the lakehouse and data warehouse, which you have brought in and transformed, to train your model.
- Data cleansing and preparation: Fabric offers different options for you to prepare, clean, and transform your data before you train your model efficiently. For example, if you prefer a user interface experience, you can use Data Wrangler, with its intuitive interface...
