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The term Hadoop is often used for both base modules and sub-modules and also the ecosystem, [12] or collection of additional software packages that can be installed on top of or alongside Hadoop, such as Apache Pig, Apache Hive, Apache HBase, Apache Phoenix, Apache Spark, Apache ZooKeeper, Apache Impala, Apache Flume, Apache Sqoop, Apache Oozie ...
Sqoop is a command-line interface application for transferring data between relational databases and Hadoop. [ 1 ] The Apache Sqoop project was retired in June 2021 and moved to the Apache Attic.
Presto (including PrestoDB, and PrestoSQL which was re-branded to Trino) is a distributed query engine for big data using the SQL query language. Its architecture allows users to query data sources such as Hadoop, Cassandra, Kafka, AWS S3, Alluxio, MySQL, MongoDB and Teradata, [1] and allows use of multiple data sources within a query.
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]
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 Replication 2020 ...
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.
2009-10-30 the project is born, and immediately named "TinkerPop" 2009-12-25 v0.1 is the first release; 2011-05-21 v1.0 is released; 2012-05-24 v2.0 is released; 2015-01-16 TinkerPop becomes an Apache Incubator project; 2015-07-09 v3.0.0-incubating is released; 2016-05-23 Apache TinkerPop becomes a top-level project
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 ...