Parquet – A columnar data table format optimized for use with big data processing frameworks such as Apache Hadoop, Apache Spark, and others, and designed to allow complex data processing operations to be performed quickly. The main features:
- Columnar Storage: Unlike row-based formats (like CSV), Parquet stores data in separate columns, which can be accessed and analysed omitted their own.
- Compression: Compression techniques (like Snappy, Gzip, or LZO) reduce file size more effectively than row-based formats.
- Data Encoding: It employs encoding schemes (e.g., dictionary encoding, run-length encoding) to further optimize storage and speed up queries by reducing data redundancy.
- Metadata: Parquet files include embedded metadata that describes the schema, column statistics (like min/max values), and other details, enabling query engines to skip irrelevant data, improving performance.
- Predicate Pushdown: Because of its metadata and columnar structure, Parquet supports predicate pushdown, where filters (e.g., “WHERE age > 30”) are applied at the storage level, reducing the amount of data scanned.
- Compatibility: It’s widely supported across data processing ecosystems, for data lakes and large-scale analytics.
Parquet is meant for use with large datasets used in data warehouses and machine learning pipelines, not for small, simple datasets or data tables featuring frequent row-level updates. It’s optimized for batch processing rather than transactional workloads.
