What is Parquet?

Overview

Parquet is a columnar storage format for big data processing systems, such as Apache Hadoop and Apache Spark. It is designed to provide efficient storage, compression, and encoding of data, while also allowing for fast querying and data retrieval.

Parquet is built on top of the Google Dremel paper, which proposed a columnar storage format for big data processing systems. The columnar storage format allows for better compression and encoding of data, as well as more efficient querying and data retrieval.

One of the main benefits of using Parquet is its ability to compress and encode data in a way that reduces storage space and increases query performance. This is achieved through a combination of techniques such as dictionary encoding, run-length encoding, and bit-packing.

Another benefit of Parquet is its ability to support complex data structures and nested data. This allows for the storage of semi-structured and nested data, such as JSON and Avro data, in a more efficient and organized way.

Parquet also supports predicate pushdown, which allows for filtering of data at the storage level. This can greatly increase query performance, particularly when working with large data sets.

Parquet is a widely used file format and supported by many big data systems and technologies like Apache Hadoop, Apache Spark, Hive, Impala, Presto, Drill, and many more. It can be used in both batch and streaming scenarios and it is also used to store data in cloud storage like Amazon S3, Google Cloud Storage, and Microsoft Azure Data Lake Storage.

Summary

In summary, Parquet is a columnar storage format for big data processing systems, such as Apache Hadoop and Apache Spark. It is designed to provide efficient storage, compression, and encoding of data, while also allowing for fast querying and data retrieval. Its support for complex data structures, nested data, and predicate pushdown makes it a popular choice for storing and processing big data.

Related tags: Apache Hadoop, Apache Spark, Parquet, Columnar Storage, Compression, Encoding, Predicate Pushdown, Big Data, Data Warehousing, Data Lake, Hadoop, Spark SQL, Hive, Impala, Presto, Drill, Amazon S3, Google Cloud Storage, Microsoft Azure Data Lake Storage.