Parquet has become a widely adopted format in Big Data due to its column-oriented nature that significantly enhances query execution speed and reduces storage requirements through effective compression methods. When paired with modern storage solutions like Delta Lake or Apache Iceberg, it integrates seamlessly with various query engines and cloud data warehouses, improving overall data processing capabilities. The article emphasizes the use of PyArrow for creating Parquet files, highlighting the benefits of its parameter tuning for optimal file manipulation and performance in data systems.
Parquet has emerged as a standard format for efficient data storage in Big Data ecosystems due to its column-oriented structure, enabling faster query performance.
Combining Parquet with storage frameworks such as Delta Lake enhances its integration with various query engines like Trino, leading to improved performance in data processing.
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