Apache Parquet

About Apache Parquet

A columnar storage format available to any project in the Hadoop ecosystem. Parquet provides efficient compression and encoding schemes, making it the de facto standard for analytical workloads in data lakes and warehouses.

Key Features

• 1Columnar storage format optimized for analytical read patterns
• 2Column-level compression reduces file sizes by 5-10x vs CSV
• 3Predicate pushdown enables skipping irrelevant row groups without reading them
• 4Nested data model supports lists, maps, and struct columns
• 5Standard format in Hadoop, Spark, BigQuery, Athena, and Snowflake ecosystems

How Python Data Engineers Use Apache Parquet

Parquet is the standard output format for Python data pipelines writing to a data lake. Engineers use `pandas.to_parquet()` or `pyarrow.parquet.write_table()` to write DataFrames as efficiently compressed columnar files. Reading is equally simple — `pd.read_parquet('s3://bucket/prefix/')` reads an entire partitioned dataset, with DuckDB and Athena capable of querying Parquet files directly without loading.