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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.
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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 .
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.
ORC: 0 orc Apache ORC (Optimized Row Columnar) file format 4F 62 6A 01: Obj␁ 0 avro Apache Avro binary file format 53 45 51 36: SEQ6: 0 rc RCFile columnar file format 3C 72 6F 62 6C 6F 78 21 <roblox! 0 rbxl Roblox place file [71] 65 87 78 56: e‡xV: 0 p25 obt PhotoCap Object Templates 55 55 AA AA: UUªª: 0 pcv PhotoCap Vector 78 56 34: xV4 ...
Apache Mesos: Apache actively developed Apache license v2.0 Linux Free Yes Moab Cluster Suite: Adaptive Computing Job Scheduler actively developed HPC Proprietary: Linux, Mac OS X, Windows, AIX, OSF/Tru-64, Solaris, HP-UX, IRIX, FreeBSD & other UNIX platforms Cost Yes NetworkComputer: Runtime Design Automation actively developed HTC/HPC Proprietary
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]