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  2. Apache Hadoop - Wikipedia

    en.wikipedia.org/wiki/Apache_Hadoop

    [8] [9] The base Apache Hadoop framework is composed of the following modules: Hadoop Common – contains libraries and utilities needed by other Hadoop modules; Hadoop Distributed File System (HDFS) – a distributed file-system that stores data on commodity machines, providing very high aggregate bandwidth across the cluster;

  3. Distributed file system for cloud - Wikipedia

    en.wikipedia.org/wiki/Distributed_file_system...

    Google File System (GFS) and Hadoop Distributed File System (HDFS) are specifically built for handling batch processing on very large data sets. For that, the following hypotheses must be taken into account: [9] High availability: the cluster can contain thousands of file servers and some of them can be down at any time

  4. Comparison of distributed file systems - Wikipedia

    en.wikipedia.org/wiki/Comparison_of_distributed...

    Reed-Solomon [9] File [10] 2005 IPFS: Go Apache 2.0 or MIT HTTP gateway, FUSE, Go client, Javascript client, command line tool: Yes with IPFS Cluster: Replication [11] Block [12] 2015 [13] JuiceFS: Go Apache License 2.0 POSIX, FUSE, HDFS, S3: Yes Yes Reed-Solomon Object 2021 Kertish-DFS: Go GPLv3 HTTP(REST), CLI, C# Client, Go Client Yes ...

  5. GPFS - Wikipedia

    en.wikipedia.org/wiki/GPFS

    GPFS distributes its directory indices and other metadata across the filesystem. Hadoop, in contrast, keeps this on the Primary and Secondary Namenodes, large servers which must store all index information in-RAM. GPFS breaks files up into small blocks. Hadoop HDFS likes blocks of 64 MB or more, as this reduces the storage requirements of the ...

  6. Apache Avro - Wikipedia

    en.wikipedia.org/wiki/Apache_Avro

    Avro is a row-oriented remote procedure call and data serialization framework developed within Apache's Hadoop project. It uses JSON for defining data types and protocols, and serializes data in a compact binary format.

  7. Data-intensive computing - Wikipedia

    en.wikipedia.org/wiki/Data-intensive_computing

    Hadoop now encompasses multiple subprojects in addition to the base core, MapReduce, and HDFS distributed filesystem. These additional subprojects provide enhanced application processing capabilities to the base Hadoop implementation and currently include Avro, Pig , HBase , ZooKeeper , Hive , and Chukwa.

  8. Apache HBase - Wikipedia

    en.wikipedia.org/wiki/Apache_HBase

    Tables in HBase can serve as the input and output for MapReduce jobs run in Hadoop, and may be accessed through the Java API but also through REST, Avro or Thrift gateway APIs. HBase is a wide-column store and has been widely adopted because of its lineage with Hadoop and HDFS. HBase runs on top of HDFS and is well-suited for fast read and ...

  9. Apache Pig - Wikipedia

    en.wikipedia.org/wiki/Apache_Pig

    Apache Pig [1] is a high-level platform for creating programs that run on Apache Hadoop. The language for this platform is called Pig Latin. [1] Pig can execute its Hadoop jobs in MapReduce, Apache Tez, or Apache Spark. [2]

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