Which data formats are primarily supported by Amazon Athena for querying?

Which data formats are primarily supported by Amazon Athena for querying?

• A. PDF, XML, and TXT
• B. CSV, JSON, Parquet, and ORC
• C. Only structured data formats
• D. Word and image files

Answer: (B)

Amazon Athena is designed specifically for querying data stored in various data formats, and it excels with columnar storage formats that optimize read performance. The supported data formats include CSV, JSON, Parquet, and ORC. These formats are chosen for their efficient layout and capabilities to handle large datasets, making them ideal for analytical queries. CSV is a common text format for tabular data and is widely used due to its simplicity. JSON is favored for its flexibility and ability to represent hierarchical data structures. Parquet and ORC are columnar storage formats that offer advanced optimizations, such as better compression and faster query performance, especially when working with large datasets. In contrast, the other options listed are not suitable for querying in Amazon Athena. PDF and Word files are document formats that are not structured for analytical querying. XML can be queried with Athena but is not as commonly utilized; it lacks the performance optimization provided by formats like Parquet and ORC. Additionally, data formats that include images or other non-text files are not supported by Athena for querying purposes. Therefore, the focus on CSV, JSON, Parquet, and ORC aligns with Athena's strengths in data analytics.

Amazon Athena is designed specifically for querying data stored in various data formats, and it excels with columnar storage formats that optimize read performance. The supported data formats include CSV, JSON, Parquet, and ORC. These formats are chosen for their efficient layout and capabilities to handle large datasets, making them ideal for analytical queries.

CSV is a common text format for tabular data and is widely used due to its simplicity. JSON is favored for its flexibility and ability to represent hierarchical data structures. Parquet and ORC are columnar storage formats that offer advanced optimizations, such as better compression and faster query performance, especially when working with large datasets.

In contrast, the other options listed are not suitable for querying in Amazon Athena. PDF and Word files are document formats that are not structured for analytical querying. XML can be queried with Athena but is not as commonly utilized; it lacks the performance optimization provided by formats like Parquet and ORC. Additionally, data formats that include images or other non-text files are not supported by Athena for querying purposes. Therefore, the focus on CSV, JSON, Parquet, and ORC aligns with Athena's strengths in data analytics.