Introduction to Parquet

Parquet is a columnar storage format for big data processing. It is widely used in the Apache Hadoop ecosystem, as well as other big data frameworks, and is known for its ability to efficiently store and process large amounts of data.

For more information about the Parquet file format, also checkout: https://parquet.apache.org/ to learn more.

Parquet File Structure

A parquet file is a self-contained binary file that is optimized for columnar storage. Unlike row-based storage formats, where all data for a row is stored together, in a parquet file each column is stored separately.

Advantages of Parquet

Parquet is designed to be highly compressed, which means that it can store a large amount of data in a compact form, making it faster and more efficient to read and process.

It supports a wide range of data types, including integers, floating-point numbers, strings, and binary data.

Parquet in Big Data

Parquet is optimized for use with big data processing frameworks like Apache Spark and Apache Hive, which makes it an ideal choice for storing structured and semi-structured data.

Conclusion

In conclusion, parquet is a powerful and versatile columnar storage format that is ideal for big data processing. With its combination of compression, flexibility, and optimization for big data frameworks, it is no wonder that it has become the preferred choice for many big data practitioners.