Which Azure service is primarily used for data preparation and cleaning?

Azure Data Factory is primarily used for data preparation and cleaning due to its robust capabilities in data integration. It allows users to create data pipelines that can extract, transform, and load (ETL) data from various sources. This includes moving data between different storage systems, applying transformations to ensure data consistency and quality, and scheduling these processes to run at specified intervals or triggers.

The service is designed to handle large volumes of data, enabling data engineers and data scientists to orchestrate workflows effectively. Through its visual interface, users can easily map data flow and transformation rules, which makes it accessible for users who may not have extensive coding experience. Additionally, Azure Data Factory can integrate with other Azure services, enhancing its ability to clean and prepare data for advanced analytics and machine learning processes.

The other services, while powerful in their respective domains, focus on different aspects of the data lifecycle. Azure Databricks is more geared towards collaborative analytics and machine learning using Apache Spark. Azure Synapse Analytics combines data warehousing and big data analytics, making it suited for complex analytical queries rather than stand-alone data preparation tasks. Azure Machine Learning provides tools and frameworks specifically for building, training, and deploying machine learning models, but data preparation is a step that typically leverages services