![]() Self-describing : In addition to data, a Parquet file contains metadata including schema and structure. To quote the project website, “Apache Parquet is… available to any project… regardless of the choice of data processing framework, data model, or programming language.”ģ. Open-source: Parquet is free to use and open source under the Apache Hadoop license, and is compatible with most Hadoop data processing frameworks. Columnar: Unlike row-based formats such as CSV or Avro, Apache Parquet is column-oriented – meaning the values of each table column are stored next to each other, rather than those of each record:Ģ. Basic Definition: What is Apache Parquet?Īpache Parquet is a file format designed to support fast data processing for complex data, with several notable characteristics:ġ. Now, let’s take a closer look at what Parquet actually is, and why it matters for big data storage and analytics. You can execute sample pipeline templates, or start building your own, in Upsolver for free. It can input and output Parquet files, and uses Parquet as its default storage format. ![]() In fact, Parquet is one of the main file formats supported by Upsolver, our all-SQL platform for transforming data in motion. It’s clear that Apache Parquet plays an important role in system performance when working with data lakes. Converting data to columnar formats such as Parquet or ORC is also recommended as a means to improve the performance of Amazon Athena. When AWS announced data lake export, they described Parquet as “2x faster to unload and consumes up to 6x less storage in Amazon S3, compared to text formats”. Since it was first introduced in 2013, Apache Parquet has seen widespread adoption as a free and open-source storage format for fast analytical querying. Get the full resource for insights into the distinctions between ORC and Parquet file formats, including their optimal use cases. The following is an excerpt from our guide to big data file formats. Apache Parquet Use Cases – When Should You Use It?.Column-Oriented vs Row-Based Storage for Analytic Querying.Advantages of Parquet Columnar Storage – Why Should You Use It?.Basic Definition: What is Apache Parquet?.
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