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In Neptune ML, the connectivity for data processing is established through data pipelines. Data pipelines are workflows that connect various stages of data processing together. These stages can include data ingestion, data transformation, and data output. The pipelines also define the dependencies between these stages and regulate the flow of data between them. Neptune ML uses Apache Airflow to create and manage these pipelines, making it easy to automate and scale data processing tasks. Additionally, Neptune ML allows for integration with various data sources, such as Hadoop, Spark, and Amazon S3, to facilitate the seamless flow of data between these sources and the data pipelines.