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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;
Cascading is a software abstraction layer for Apache Hadoop and Apache Flink. Cascading is used to create and execute complex data processing workflows on a Hadoop cluster using any JVM-based language (Java, JRuby, Clojure, etc.), hiding the underlying complexity of MapReduce jobs. It is open source and available under the Apache License.
Apache HBase began as a project by the company Powerset out of a need to process massive amounts of data for the purposes of natural-language search. Since 2010 it is a top-level Apache project. Facebook elected to implement its new messaging platform using HBase in November 2010, but migrated away from HBase in 2018. [4]
Hortonworks and Red Hat Extend Collaboration, Innovate within the Apache Software Foundation Community Open Source Initiative to Accelerate Apache Hadoop Adoption Across the Enterprise PALO ALTO ...
Hadoop: Java software framework that supports data intensive distributed applications; HAWQ: advanced enterprise SQL on Hadoop analytic engine; HBase: Apache HBase software is the Hadoop database. Think of it as a distributed, scalable, big data store
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.
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.
This framework allows applications based on the MapReduce paradigm to be run on large clusters of commodity hardware. Cutting was an employee of Yahoo! , where he led the Hadoop project full-time; he later went on to work for Cloudera .