Apache Parquet is a free and open-source column-oriented Data Lake File Format within the Apache Hadoop ecosystem. It operates similarly to RCFile and ORC, other columnar storage file formats in Hadoop, ensuring compatibility with most data processing frameworks associated with Hadoop.

Apache Parquet was officially released on 13 March 2013, marking a significant advancement in the efficiency of data storage. The format was designed to offer an optimized and flexible solution for handling large data volumes typical in big data scenarios.

Initial release

Chapter I: The Birth of Parquet | The Sympathetic Ink Blog

A noteworthy article by DuckDB Labs details Parquet’s significant role in modern data management: 42.parquet – A Zip Bomb for the Big Data Age - DuckDB

Parquet is distinguished by its efficient use of Columnar storage, which minimizes I/O operations and enables better data compression ratios and encoding schemes. This format is particularly beneficial for analytical queries that process a substantial number of rows yet access only a subset of columns.

Parquet is widely adopted in various big data tools and frameworks, enhancing data interoperability and performance across diverse ecosystems. Its integration into platforms like Apache Spark and Hadoop and tools such as Pandas and Apache Arrow exemplify its versatility and robustness in handling complex data operations.

This entry ensures a comprehensive understanding of Apache Parquet, emphasizing its historical context, technical advantages, and broad applications in data engineering.

Nimble and Lance from Nimble and Lance: The Parquet Killers - by Chris Riccomini.

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Created 2022-08-16