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The core of Apache Hadoop consists of a storage part, known as Hadoop Distributed File System (HDFS), and a processing part which is a MapReduce programming model. Hadoop splits files into large blocks and distributes them across nodes in a cluster. It then transfers packaged code into nodes to process the data in parallel.
Exports can be used to put data from Hadoop into a relational database. Sqoop got the name from "SQL-to-Hadoop". [4] Sqoop became a top-level Apache project in March 2012. [5] Informatica provides a Sqoop-based connector from version 10.1.
Apache Kylin is an open source distributed analytics engine designed to provide a SQL interface and multi-dimensional analysis (OLAP) on Hadoop and Alluxio supporting extremely large datasets. It was originally developed by eBay , and is now a project of the Apache Software Foundation .
HBase is an open-source non-relational distributed database modeled after Google's Bigtable and written in Java.It is developed as part of Apache Software Foundation's Apache Hadoop project and runs on top of HDFS (Hadoop Distributed File System) or Alluxio, providing Bigtable-like capabilities for Hadoop.
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 .
It using the hadoop file system as distributed storage. Tiles: templating framework built to simplify the development of web application user interfaces. Trafodion: Webscale SQL-on-Hadoop solution enabling transactional or operational workloads on Apache Hadoop [11] [12] [13] Tuscany: SCA implementation, also providing other SOA implementations
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 ]
Cutting and Mike Cafarella, realizing the importance of this paper to extending Lucene into the realm of extremely large search problems, created the open-source Hadoop framework. This framework allows applications based on the MapReduce paradigm to be run on large clusters of commodity hardware.
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