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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 ...
The project was announced in October 2012 with a public beta test distribution [3] [4] and became generally available in May 2013. [ 5 ] Impala brings scalable parallel database technology to Hadoop, enabling users to issue low-latency SQL queries to data stored in HDFS and Apache HBase without requiring data movement or transformation.
HDFS: Hadoop's own rack-aware file system. [47] This is designed to scale to tens of petabytes of storage and runs on top of the file systems of the underlying operating systems. Apache Hadoop Ozone: HDFS-compatible object store targeting optimized for billions of small files. FTP file system: This stores all its data on remotely accessible FTP ...
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]
Spark Core is the foundation of the overall project. It provides distributed task dispatching, scheduling, and basic I/O functionalities, exposed through an application programming interface (for Java, Python, Scala, .NET [16] and R) centered on the RDD abstraction (the Java API is available for other JVM languages, but is also usable for some other non-JVM languages that can connect to the ...
Bahir: extensions to distributed analytic platforms such as Apache Spark; Beam, an uber-API for big data; Bigtop: a project for the development of packaging and tests of the Apache Hadoop ecosystem. Bloodhound: defect tracker based on Trac [5] BookKeeper: a reliable replicated log service
Early data lakes, such as Hadoop 1.0, had limited capabilities because it only supported batch-oriented processing . Interacting with it required expertise in Java, map reduce and higher-level tools like Apache Pig , Apache Spark and Apache Hive (which were also originally batch-oriented).
Apache Mahout is a project of the Apache Software Foundation to produce free implementations of distributed or otherwise scalable machine learning algorithms focused primarily on linear algebra. In the past, many of the implementations use the Apache Hadoop platform, however today it is primarily focused on Apache Spark.