Apache Hadoop is an excellent framework for processing, storing and analyzing large volumes of unstructured data - aka Big Data. But getting a handle on all the project’s myriad components and sub-components, with names like Pig and Mahout, can be a difficult.
Hadoop Distributed File System: HDFS, the storage layer of Hadoop, is a distributed, scalable, Java-based file system adept at storing large volumes of unstructured data.
MaPReduce - MapReduce is a software framework that serves as the compute layer of Hadoop. MapReduce jobs are divided into two (obviously named) parts. The “Map” function divides a query into multiple parts and processes data at the node level. The “Reduce” function aggregates the results of the “Map” function to determine the “answer” to the query.
Hive: Hive is a Hadoop-based data warehousing-like framework originally developed by Facebook. It allows users to write queries in a SQL-like language caled HiveQL, which are then converted to MapReduce. This allows SQL programmers with no MapReduce experience to use the warehouse and makes it easier to integrate with business intelligence and visualization tools such as Microstrategy, Tableau, Revolutions Analytics, etc.
Pig: Pig Latin is a Hadoop-based language developed by Yahoo. It is relatively easy to learn and is adept at very deep, very long data pipelines (a limitation of SQL.)
HCatalog: HCatalog is a centralized metadata management and sharing service for Apache Hadoop. It allows for a unified view of all data in Hadoop clusters and allows diverse tools, including Pig and Hive, to process any data elements without needing to know physically where in the cluster the data is stored.
Tez - Extensible framework for building high performance batch and interactive data processing applications, coordinated by YARN in Apache Hadoop. Tez improves the MapReduce paradigm by dramatically improving its speed, while maintaining MapReduce’s ability to scale to petabytes of data
HBase: HBase is a non-relational database that allows for low-latency, quick lookups in Hadoop. It adds transactional capabilities to Hadoop, allowing users to conduct updates, inserts and deletes. EBay and Facebook use HBase heavily.
Sqoop: Sqoop is a connectivity tool for moving data from non-Hadoop data stores – such as relational databases and data warehouses – into Hadoop. It allows users to specify the target location inside of Hadoop and instruct Sqoop to move data from Oracle, Teradata or other relational databases to the target.
Storm - Apache Storm is a free and open source distributed realtime 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, and is a lot of fun to use
Flume: Flume is a framework for populating Hadoop with data. Agents are populated throughout ones IT infrastructure – inside web servers, application servers and mobile devices, for example – to collect data and integrate it into Hadoop.
Oozie: Oozie is a workflow processing system that lets users define a series of jobs written in multiple languages – such as Map Reduce, Pig and Hive -- then intelligently link them to one another. Oozie allows users to specify, for example, that a particular query is only to be initiated after specified previous jobs on which it relies for data are completed.
Avro: Avro is a data serialization system that allows for encoding the schema of Hadoop files. It is adept at parsing data and performing removed procedure calls.
Mahout: Mahout is a data mining library. It takes the most popular data mining algorithms for performing clustering, regression testing and statistical modeling and implements them using the Map Reduce model.
Ambari: Ambari is a web-based set of tools for deploying, administering and monitoring Apache Hadoop clusters. It's development is being led by engineers from Hortonworoks, which include Ambari in its Hortonworks Data Platform.
BigTop: BigTop is an effort to create a more formal process or framework for packaging and interoperability testing of Hadoop's sub-projects and related components with the goal improving the Hadoop platform as a whole.