Parquet file is a columnar file format helpful in storing and processing the data systematically for rapid assimilation of information mainly used in big data infrastructures such as Apache Spark and Hadoop. Its main advantages are efficient use of space via compression, which also decreases the time taken to transfer data; effective read capabilities suited for analytical functions; and features enabling changes in data structure without necessarily rewriting older data. Also, working with the Parquet file is easier for a number of data processing frameworks. In a number of ways, this makes the Parquet file format more appealing to data engineers and analysts. Every so often, Parquet files have great advantages in treatment of bulk data, giving maximum service in analytics.

A Parquet file is a columnar storage file format used in the Big Data ecosystem, designed for efficient data storage and processing. It organizes data into columns rather than rows, allowing for better compression and faster query performance.

Advantages of Parquet files include:

1. Efficient Compression: Parquet uses advanced compression techniques, reducing storage space and improving read performance.
2. Columnar Storage: Data is stored in columns, enabling selective columnar reads and minimizing I/O operations.
3. Schema Evolution: Supports schema evolution, making it easier to add or modify data fields without affecting existing data.
4. Compatibility: Widely supported in Big Data frameworks like Apache Spark, Hive, and Impala.
5. Performance: Parquet files enhance query performance due to their optimized storage layout.