Apache Parquet
Apache Parquet is a columnar storage format for Hadoop-based data processing systems, including Apache Hadoop, Apache Spark, and Apache Hive. The Parquet format is designed to support efficient, high-performance data processing for large-scale data sets, particularly in big data analytics and warehousing.
The Apache Software Foundation (ASF) developed parquet as an open-source project. It is now used by many organizations and data processing platforms as a standard format for storing and processing data. The format is particularly well-suited for analytical workloads, as it supports efficient columnar storage and compression techniques that enable faster query processing and reduced storage requirements.
Some key features of Apache Parquet include:
- Columnar storage: Data is stored in a columnar format, which can provide significant performance benefits for analytical queries and reduce I/O requirements.
- Compression: Parquet supports a range of compression techniques, including Snappy, Gzip, and LZO, which can help to reduce storage requirements and improve query performance.
- Schema evolution: Parquet supports schema evolution, which enables data structures to evolve without requiring significant changes to existing data or queries.
- Cross-platform support: Parquet can be used with various data processing platforms, including Apache Hadoop, Apache Spark, and Apache Hive.
- Language support: Parquet supports a range of programming languages, including Java, Python, and C++, and it can be easily integrated with other data processing frameworks.
Apache Parquet is a powerful and flexible data storage format that can help organizations to improve the performance and scalability of their big data processing systems. Whether you are building a data warehouse, processing large-scale data sets, or performing advanced analytics, Parquet provides a powerful tool for efficient and effective data storage and processing.