Parquet is a columnar storage format that is designed to work with big data processing frameworks like Apache Hadoop and Apache Spark. It has several advantages over traditional row-based storage formats, such as CSV and JSON.
- Columnar storage: In a columnar storage format, data is stored in a way that is optimized for reading and writing column-wise, rather than row-wise. This allows for more efficient storage and retrieval of data, as well as better compression and encoding.
- Improved compression: Parquet uses various encoding techniques like Run Length Encoding(RLE),Delta encoding and other techniques to compress the data. This results in smaller file sizes, which means less storage space is required and faster data transfer.
- Predicate pushdown: Parquet supports predicate pushdown, which means that when a query is executed, the filtering conditions are pushed down to the storage layer, rather than being processed by the query engine. This results in faster query performance and reduced data movement.
- Schema evolution: Parquet supports schema evolution, which means that the schema of a Parquet file can be modified without rewriting the entire file. This makes it easy to add new columns or change the data types of existing columns without incurring the cost of rewriting the entire file.
- Better performance: Parquet is designed to work with big data processing frameworks like Apache Hadoop and Apache Spark. When used with these frameworks, it provides better performance than row-based storage formats due to the columnar storage, improved compression, and predicate pushdown.
- Language Support : Parquet is supported by a wide range of programming languages like Java, Python, C++, and C#, which makes it easy to read, write and process data in any language.
- Interoperability: Parquet files are self-describing, which means that the schema is stored with the data. This makes it easy to share and exchange data between different systems, as the schema is always available for data reading.
- Data Governance: Parquet is also used for data governance and compliance as it supports fine-grained access controls and data encryption, making it a good option for storing sensitive data.
Parquet is a highly efficient and versatile storage format that is well-suited to big data processing and analytics. Its columnar storage, improved compression, predicate pushdown, schema evolution, better performance, Interoperability, Data Governance and language support makes it a great option for storing and processing large datasets.