Databricks is a cloud-native solution that provides high-performance and scalable data storage, analysis, and management tools for both structured and unstructured data. It is designed as a data lakehouse, combining the features of data lakes and warehouses, allowing organizations to store and manage both types of data in one platform. To fully leverage Databricks' capabilities, organizations need to develop an approach to ETL (Extract, Transform, Load) pipelines that can migrate their data into the platform. Understanding Databricks' architecture, including its base-layer object storage, Delta Lake virtual tables, and Delta Engine query engine, is crucial for building effective ETL pipelines. Two common approaches to setting up ETL pipelines are using Databricks' built-in tool, Auto Loader, or third-party ETL tools like CData Sync, which provide simplified data movement and automation capabilities. By leveraging these tools and approaches, organizations can efficiently populate their Databricks platform with data from various sources, enabling efficient analysis, reporting, and other data consumption operations.