1) Lily: Smart data, at scale, made easy http://www.lilyproject.org/lily/index.html
2) 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.
3) 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.
4) Hive: Hive is a Hadoop-based data warehouse developed by Facebook. It allows users to write queries in SQL, 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.
5) 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.)
6) 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.
7) 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.
8) 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.
9) Whirr: Whirr is a set of libraries that allows users to easily spin-up Hadoop clusters on top of Amazon EC2, Rackspace or any virtual infrastructure. It supports all major virtualized infrastructure vendors on the market.
10) 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.
11) 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.
12) 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.
13) 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.
14) Spark: Spark is a scalable data analytics platform that incorporates primitives for in-memory computing and therefore exercises some performance advantages over Hadoop’s cluster storage approach. It is implemented in Scala, which provides a unique environment for data processing. It is used by the applications which requires to iterate a set of read-only datasets many times to finish the interactive machine learning jobs. The performance of the first iteration is lower than Hadoop, then as the intermediate result is reused, the following iterations shows much better performance. The three typical features in it are: Resilient distributed datasets (RDDs), broadcast variables and accumulator. It relies on Mesos (cluster manager) for resource sharing and isolation.