Parquet is a columnar storage file format commonly used in the big data ecosystem, particularly with tools like Apache Hadoop and Apache Spark. Parquet files are designed to store structured data efficiently, making them suitable for use with data processing frameworks.
Some of its key features and characteristics are:
- Columnar Storage: Parquet stores data in a columnar format, which means that each column is stored separately. This columnar organization improves query performance and compression, as it’s more efficient for operations that involve specific columns.
- Compression: Parquet files use various compression techniques to reduce storage space and improve read performance. Different compression algorithms can be used, such as Snappy, Gzip, and LZO.
- Schema Evolution: Parquet files can support schema evolution, allowing you to add, remove, or modify columns without breaking compatibility with existing data.
- Platform Agnostic: Parquet is designed to be platform-agnostic, meaning you can use it with different programming languages and data processing frameworks. It’s a popular choice for storing data in the Hadoop ecosystem, but it’s not limited to Hadoop.
- Data Types: Parquet supports a wide range of data types, including primitive types (integers, floats, strings), complex types (structs, arrays, maps), and user-defined types.
- Performance: Due to its columnar structure and compression capabilities, Parquet is well-suited for analytical workloads and querying large datasets, making it an efficient choice for data analytics and reporting.
To open Parquet files you can use TAD or use the Python script below