Apache Parquet is an open-source, columnar storage file format optimized for use with big data processing frameworks. It is designed to provide efficient data compression and encoding schemes to improve performance of analytic workflows.
Key Features
- Columnar Storage: Stores data by columns, enabling better compression and faster scanning for specific fields.
- Efficient Compression: Supports multiple compression codecs including Snappy, Gzip, and LZO to reduce file size.
- Schema Evolution: Allows adding, dropping, or modifying columns without rewriting entire datasets.
- Splittable Files: Enables parallel processing of large datasets in distributed environments like Hadoop and Spark.
- Compatibility: Widely supported by many big data processing frameworks such as Apache Spark, Apache Hive, Apache Drill, and more.
Use Cases
Apache Parquet is commonly used in large-scale data analytics environments and is particularly well suited for:
- Data warehousing and analytics
- Interactive querying with Apache Spark and Presto
- Machine learning pipelines requiring efficient access
- Batch processing and ETL jobs
Comparison with Other Formats
Parquet is often compared with other popular formats like Apache ORC and Avro:
- Parquet vs ORC: Both are columnar storage formats optimized for analytics. Parquet is more commonly used with Spark and Impala, while ORC is tightly integrated with Hive.
- Parquet vs Avro: Avro is row-oriented and ideal for streaming, whereas Parquet is column-oriented optimized for analytical batch processing.