Databricks doesn’t directly use traditional databases like MySQL or Oracle. Instead, it leverages a concept called a Data Lakehouse for data storage and management.

Here’s a breakdown of how Databricks handles data:

• Data Lakehouse: This combines the strengths of data lakes and data warehouses. Data is stored using Delta Lake in cloud storage, providing scalability and flexibility. A megastore keeps track of the organization of data.
• Objects: The Databricks lakehouse uses several objects, including:
  • Catalogs: Groupings of databases (or schemas).
  • Databases (or Schemas): Contain tables, views, and functions.
  • Tables: The core data structure, storing data as files in cloud storage. Delta tables are the default storage option.
  • Views: Saved queries that can reference multiple tables or data sources.
  • Functions: Saved logic returning data.
• Databricks SQL: This service within Databricks provides data warehousing capabilities on the Data Lakehouse. It allows users to run SQL queries on the data using familiar syntax.

In essence, Databricks offers a way to manage and analyze data at scale, with the flexibility of a data lake and the structure of a data warehouse.