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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 .
Airavata: a distributed system software framework to manage simple to composite applications with complex execution and workflow patterns on diverse computational resources; Airflow: Python-based platform to programmatically author, schedule and monitor workflows; Allura: Python-based open source implementation of a software forge
RCFile became the default data placement structure in Facebook's production Hadoop cluster. [2] By 2010 it was the world's largest Hadoop cluster, [3] where 40 terabytes compressed data sets are added every day. [4] In addition, all the data sets stored in HDFS before RCFile have also been transformed to use RCFile . [2]
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.
BER: variable-length big-endian binary representation (up to 2 2 1024 bits); PER Unaligned: a fixed number of bits if the integer type has a finite range; a variable number of bits otherwise; PER Aligned: a fixed number of bits if the integer type has a finite range and the size of the range is less than 65536; a variable number of octets ...
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]
Apache Hive is a data warehouse software project. It is built on top of Apache Hadoop for providing data query and analysis. [ 3 ] [ 4 ] Hive gives an SQL-like interface to query data stored in various databases and file systems that integrate with Hadoop.
A HTTP/1.1-compliant, WSGI thread-pooled webserver. [4] Typically, CherryPy itself takes only 1–2 ms per page. [5] [6] Support for any other WSGI-enabled web server or adapter, including Apache, IIS, lighttpd, mod_python, FastCGI, SCGI, and mod_wsgi. A native mod_python adapter. Multiple HTTP servers (e.g. ability to listen on multiple ports ...