Apache Hadoop, at its core, consists of two components – Hadoop Distributed File System and Hadoop MapReduce. HDFS is the primary storage system used by Hadoop applications. HDFS creates multiple replicas of data blocks and distributes them on compute nodes throughout a cluster to enable reliable, extremely rapid computations. Hadoop MapReduce is a programming model and software framework for writing applications that rapidly process huge amounts of data in parallel on large clusters of compute nodes. Other Hadoop-related projects (also called EcoSystems) at Apache include Hive, Pig, HBase, Yarn, Mahout, Oozie, Sqoop, Avro, Cascading, ZooKeeper, Flume, Drill, etc.
Other competing technologies of Haddop are - Google Dremel, HPCC Systems, Apache Storm.
Google Dremel is a distributed system developed at Google for interactively querying large datasets and powers Google's BigQuery service.
HPCC (High Performance Computing Cluster) is a massive parallel-processing computing platform that solves Big Data problems.
Apache Storm is a free and open source distributed real-time computation system. Storm makes it easy to reliably process unbounded streams of data, doing for real-time processing what Hadoop did for batch processing. Storm is simple, can be used with any programming language.
Hadoop distributions are provided by growing numbers of companies. They provide products that include Apache Hadoop, a derivative work thereof, commercial support, and/or tools and utilities related to Hadoop. Some major hadoop distribution companies are - Cloudera, Hortonworks, MapR, Amazon Web services, Intel, EMC, IBM, etc.