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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.
HIVE Logo. The High-performance Integrated Virtual Environment (HIVE) is a distributed computing environment used for healthcare-IT and biological research, including analysis of Next Generation Sequencing (NGS) data, preclinical, clinical and post market data, adverse events, metagenomic data, etc. [1] Currently it is supported and continuously developed by US Food and Drug Administration ...
Release notes detail the corrections, changes or enhancements (functional or non-functional) made to the service or product the company provides.[7] [8] [9]They might also be provided as an artifact accompanying the deliverables for System Testing and System Integration Testing and other managed environments especially with reference to an information technology organization.
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.
Computer programs that can work offline, i.e. does not necessarily require network connection to function; Software that is not a part of some bundled software; A program that run as a separate computer process, not an add-on of an existing process; Standalone program, a program that does not require operating system's services to run
The following benchmarks have been performed on Fraunhofer Seislab, [13] a test and experimental cluster at Fraunhofer ITWM with 25 nodes (20 compute plus 5 storage) and a three-tier memory: 1 TB RAM, 20 TB SSD, 120 TB HDD. Single node performance on the local file system without BeeGFS is 1,332 MB/s (write) and 1,317 MB/s (read).
In computing, a minimal reproducible example (abbreviated MRE) [1] is a collection of source code and other data files which allow a bug or problem to be demonstrated and reproduced. The important feature of a minimal reproducible example is that it is as small and as simple as possible, such that it is just sufficient to demonstrate the ...
The Dataframe API was released as an abstraction on top of the RDD, followed by the Dataset API. In Spark 1.x, the RDD was the primary application programming interface (API), but as of Spark 2.x use of the Dataset API is encouraged [3] even though the RDD API is not deprecated. [4] [5] The RDD technology still underlies the Dataset API. [6] [7]