Sqoop is a tool designed to import data from relational databases into Hadoop. Sqoop uses JDBC to connect to a database. It examines each table’s schema and automatically generates the necessary classes to import data into the Hadoop Distributed File System (HDFS). Sqoop then creates and launches a MapReduce job to read tables from the database via DBInputFormat, the JDBC-based InputFormat. Tables are read into a set of files in HDFS. Sqoop supports both SequenceFile and text-based target and includes performance enhancements for loading data from MySQL.
HBase: Bigtable-like structured storage for Hadoop HDFS Just as Google's [WWW] Bigtable leverages the distributed data storage provided by the [WWW] Google File System, HBase provides Bigtable-like capabilities on top of Hadoop Core. Data is organized into tables, rows and columns. An Iterator-like interface is available for scanning through a row range (and of course there is the ability to retrieve a column value for a specific key). Any particular column may have multiple versions for the same row key.
Katta is a scalable, failure tolerant, distributed, data storage for real time access.
Katta serves large, replicated, indices as shards to serve high loads and very large data sets. These indices can be of different type. Currently implementations are available for Lucene and Hadoop mapfiles.
* Makes serving large or high load indices easy
* Serves very large Lucene or Hadoop Mapfile indices as index shards on many servers
* Replicate shards on different servers for performance and fault-tolerance
* Supports pluggable network topologies
* Master fail-over
* Fast, lightweight, easy to integrate
* Plays well with Hadoop clusters
* Apache Version 2 License
Pig is a platform for analyzing large data sets that consists of a high-level language for expressing data analysis programs, coupled with infrastructure for evaluating these programs. The salient property of Pig programs is that their structure is amenable to substantial parallelization, which in turns enables them to handle very large data sets. At the present time, Pig's infrastructure layer consists of a compiler that produces sequences of Map-Reduce programs, for which large-scale parallel implementations already exist Pig's language layer currently consists of a textual language called Pig Latin, which has the following key properties: * Ease of programming. It is trivial to achieve parallel execution of simple, "embarrassingly parallel" data analysis tasks. * Optimization opportunities. The way in which tasks are encoded permits the system to optimize their execution automatically * Extensibility.