Apache Parquet is a columnar storage format optimized for use with big data processing frameworks. Unlike row-oriented formats, Parquet stores data by columns, which allows for efficient data compression and encoding. This columnar storage enables query engines to retrieve only the necessary columns for a given query, significantly reducing I/O and improving query performance. Parquet is designed to be self-describing, meaning that the schema is embedded within the data file itself. This eliminates the need for external metadata stores and simplifies data management. It supports a wide range of data types and complex nested structures. Parquet is widely used in data warehousing, data lakes, and other big data applications where efficient data storage and retrieval are critical. Its integration with popular frameworks like Apache Spark, Hadoop, and Presto makes it a versatile choice for data processing pipelines. The format is designed for both read and write operations, although it is often used in scenarios where data is written once and read many times (write-once, read-many).