Which format is typically used to store and share data in Azure Machine Learning?

The preferred format for storing and sharing data in Azure Machine Learning is Parquet. This columnar storage file format is optimized for large-scale data processing, making it well-suited for analytical workflows common in machine learning. Parquet files are efficient to read and write, allowing for faster data retrieval, which is crucial when working with large datasets typical in machine learning scenarios.

Additionally, Parquet's columnar format enables effective compression and encoding schemes, significantly reducing the storage footprint while maintaining performance during processing. Its compatibility with various big data tools and frameworks, including those in the Azure ecosystem, makes it an advantageous choice for data scientists working within Azure Machine Learning.

While CSV, JSON, and XML are also valid data formats, they do not provide the same level of efficiency and performance for large-scale machine learning tasks as Parquet does. CSV is widely used but can be less efficient for structured data due to its row-based format. JSON is great for data interchange but can become complex and heavy with nested structures. XML, while human-readable, tends to be more verbose and can lead to larger file sizes, which is less efficient for analytics compared to Parquet. Thus, Parquet stands out as the choice that aligns best with the needs of Azure Machine Learning