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Book Overview & Buying
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Table Of Contents
Building Modern Data Applications Using Databricks Lakehouse
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In this chapter, we covered a variety of methods for storing data while also maintaining fine-grained access control using different securable objects in Unity Catalog. We covered how data could be stored using catalogs, schemas, tables, views, volumes, and external locations in Unity Catalog. We also saw how organizations could bind catalogs to individual Databricks workspaces to isolate datasets and even set the level of access to read-only. We covered the differences between managed datasets in the Databricks Data Intelligence Platform, as well as how we could set prescribed storage locations for storing data using catalogs, schemas, tables, volumes, and external locations. We covered how external data sources, such as data warehouses, could be queried in place without having to migrate the data using Lakehouse Federation. Lastly, we concluded with a hands-on exercise implementing the start of a generative AI pipeline for extracting text from documents using volumes in Unity...