1 year ago

As the amount of data being generated and stored for analysis grows at an increasing rate, developers are looking to optimize performance and reduce costs at every angle possible. At the petabyte scale, even marginal gains and optimizations can save companies millions of dollars in hardware costs when it comes to storing and processing their data.

Parquet was designed to improve on Hadoop’s existing storage format in terms of various performance metrics like reducing the size of data on disk through compression and making reads faster for analytics queries.

A number of projects support Parquet as a file format for importing and exporting data, as well as using Parquet internally for data storage.

When data is pulled from Parquet files, it is loaded into memory using the https://www.influxdata.com/glossary/apache-arrow/?utm_source=vendor&utm_medium=referral&utm_campaign=2022-10_spnsr-ctn_intro-apache-parquet_tns format, which is also column-based so minimal performance overhead is incurred.