The correct option is Parquet.

Parquet is a compressed binary, column oriented file format that is designed for efficient analytic queries where you read only some columns. Parquet stores data by column and it supports nested data types and a schema which makes it suitable for both structured and semi structured datasets. Parquet also enables predicate pushdown and column pruning which reduce IO and improve query performance in BigQuery, Spark, and other analytics engines.

Avro is a compact binary serialization format that is row oriented rather than column oriented. Avro is excellent for streaming and record serialization but it does not provide the same IO savings for column based analytics as a columnar format.

CSV is a plain text format that lacks a typed schema and nested structure. CSV is not a compressed binary format and it is inefficient for column oriented processing at scale.

JSON is a text based semi structured format that is flexible for nested data. JSON is verbose and not columnar which makes it less efficient for large scale analytic workloads.

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