Which formats are considered types of batch data?

Batch data refers to a collection of data that is processed in groups or "batches" rather than individually and is typically characterized by being stored and processed in bulk. The correct choice identifies a comprehensive list of formats that are commonly used for batch data storage.

CSV (Comma-Separated Values) and TSV (Tab-Separated Values) are well-known formats for representing tabular data and are frequently utilized for batch data operations due to their simplicity and wide compatibility. JSON (JavaScript Object Notation) and XML (eXtensible Markup Language) are also prevalent formats for structured data, allowing for hierarchical representation, which makes them suitable for batch processing.

Parquet is a columnar storage file format that is optimized for batch processing and can significantly improve performance in data analytics scenarios. Blob files, particularly in the context of cloud storage, can encapsulate various types of data, including images, documents, and other file types, making them suitable for batch processing of large datasets.

By including all these formats—CSV, TSV, JSON, XML, Parquet, and Blob files—the selected choice encompasses a wide variety of batch data formats that are utilized across different platforms and applications, reflecting the diverse nature of batch data processing in the field of data management.