The DataHaskell organization has expanded its data science toolkit by implementing a native Parquet writer within its dataframe library. Parquet is a widely used columnar storage format that optimizes for efficient data retrieval and compression, making it a standard for interoperability across modern data science tools. Previously, Haskell developers were largely restricted to formats like CSV or JSON, which often suffer from performance limitations and lack the structural metadata required for large-scale data analysis. By enabling native Parquet support, the library allows users to write dataframes directly to disk using a simple interface, while providing advanced configuration options for fine-tuning row group and page sizes to suit specific workloads. The implementation manages memory using pinned byte arrays to handle the complexities of buffering data before it is flushed to disk, ensuring that the writer can accommodate varying data structures. While the current version supports basic flat schemas and essential compression codecs like Snappy, it remains a single-threaded implementation with plans to address memory constraints and add support for more complex nested data structures in future iterations. This development marks a step toward making Haskell a more practical and performant option for data-intensive tasks within the broader data science ecosystem.