Why for data analysis of logs stored in Amazon S3, Parquet is often considered the most suitable data format.

1. Columnar Storage: Parquet is a columnar storage format, which means that data is organized and stored column-wise rather than row-wise. This layout is highly efficient for analytical queries, especially those that involve scanning and aggregating large volumes of data. It allows for selective column reads, which can significantly reduce the amount of data scanned and improve query performance.
2. Compression: Parquet supports various compression algorithms, such as Snappy, Gzip, and LZO, which can further reduce storage costs and improve query performance. Compressed Parquet files take up less space on disk and can be read more efficiently.
3. Schema Evolution: Parquet supports schema evolution, allowing you to evolve and update the schema of your log data over time without requiring a full rewrite of existing data. This flexibility is valuable in scenarios where the schema of log data may change frequently.

While Avro and ORC are also popular data formats for data analysis, Parquet is often preferred for its superior performance, efficient storage, and schema evolution capabilities, making it well-suited for analyzing logs stored in S3.