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The provided document offers a comparative analysis between Azure Data Factory (ADF) and Azure Databricks, highlighting their roles in data processing and transformation. ADF is described as an orchestration tool primarily for ELT operations, designed for moving data across various sources and environments. In contrast, Azure Databricks is presented as a fast and scalable analytics platform built on Apache Spark, favored by data engineers and scientists for its efficiency. The sources collectively illustrate how both tools are capable of handling diverse data types, including big data, and support both batch and streaming data processing. A detailed table further outlines their respective features across categories like integration runtime, pipeline activities, and development tools, providing a clear distinction of their capabilities. Ultimately, the document aims to inform readers about the strengths and applications of each platform.