CSVParquet

CSVParquet is a term used to describe the workflows, tooling, and best practices involved in converting data between the CSV (Comma-Separated Values) format and the Apache Parquet columnar storage format. It encapsulates both the process of reading CSV files into structured data and writing that data out as Parquet files to support efficient analytics on large datasets.

CSV is a simple, row-oriented text format that stores data without a native schema or compression, while Parquet is a columnar format designed for high performance reading and compression. CSVParquet workflows aim to preserve data fidelity while gaining Parquet's benefits, such as columnar access, compression, and optimized I/O for analytical workloads.

Typical implementations read a CSV with delimiter and quote handling, infer or provide a schema, and write to Parquet with options for partitioning, row group size, and compression (such as Snappy or Zstandard). Popular tooling includes PyArrow, pandas to_parquet, Apache Spark, and various data processing libraries that bridge CSV and Parquet.

Considerations include correct type inference, handling missing values, and dealing with inconsistent rows or unusual escaping. Performance depends on chunked processing, memory management, and partitioning strategy. CSVParquet is commonly used in data lakes and warehouses to speed up analytical queries and support scalable analytics pipelines.