Microsoft Azure Data Engineer Certification (DP-203) 2025 – 400 Free Practice Questions to Pass the Exam

The Parquet file format is optimized for performance and efficiency, particularly in analytical workloads. One of its primary advantages is that it uses columnar storage, which organizes data into columns rather than rows. This format is particularly beneficial for scenarios where queries involve operations on large datasets and can lead to significant performance improvements.

Because it stores data in columns, Parquet allows for efficient compression. Data stored in the same column often contains similar values, making it more compressible compared to row-based formats. This not only saves storage space but also enhances the speed of data retrieval, as less data needs to be read from disk.

Additionally, querying performance is vastly improved with the Parquet format since it enables systems like Apache Spark and Hive to read only the necessary columns instead of the entire dataset. This selective reading reduces I/O operations and accelerates data processing times, which is crucial when working with large datasets.

In summary, the benefits of Parquet's columnar storage, efficient compression, and heightened query performance make it a valuable choice for data engineering tasks in Azure and other data processing environments.