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Apache ORC (Optimized Row Columnar) is a free and open-source column-oriented data storage format. [3] It is similar to the other columnar-storage file formats available in the Hadoop ecosystem such as RCFile and Parquet. It is used by most of the data processing frameworks Apache Spark, Apache Hive, Apache Flink, and Apache Hadoop.
RCFile became the de facto standard data storage structure in Hadoop software environment supported by the Apache HCatalog project (formerly known as Howl [10]) that is the table and storage management service for Hadoop. [11] RCFile is supported by the open source Elephant Bird library used in Twitter for daily data analytics. [12]
Apache Parquet is a free and open-source column-oriented data storage format in the Apache Hadoop ecosystem. It is similar to RCFile and ORC , the other columnar-storage file formats in Hadoop , and is compatible with most of the data processing frameworks around Hadoop .
SSL/TLS support for the IBM HTTP Server, a derivative of the Apache HTTP Server. [100] mod_imap: Version 1.2-2.0: Included by Default: Apache Software Foundation: Apache License, Version 2.0: Server side processing for Image map. This module was renamed to mod_imagemap from v2.2 onwards. [101] mod_macro: Version 1.3 and newer: Third party
By default, Hive stores metadata in an embedded Apache Derby database, and other client/server databases like MySQL can optionally be used. [12] The first four file formats supported in Hive were plain text, [13] sequence file, optimized row columnar (ORC) format [14] [15] and RCFile.
Apache Impala is an open source massively parallel processing (MPP) SQL query engine for data stored in a computer cluster running Apache Hadoop. [1] Impala has been described as the open-source equivalent of Google F1 , which inspired its development in 2012.
Apache CarbonData is a free and open-source column-oriented data storage format of the Apache Hadoop ecosystem. It is similar to the other columnar-storage file formats available in Hadoop namely RCFile and ORC. It is compatible with most of the data processing frameworks in the Hadoop environment.
Apache Parquet and Apache ORC are popular examples of on-disk columnar data formats. Arrow is designed as a complement to these formats for processing data in-memory. [11] The hardware resource engineering trade-offs for in-memory processing vary from those associated with on-disk storage. [12]