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.
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.
Apache's Hadoop project aims to solve these problems by providing a framework for running large data processing applications on clusters of commodity hardware. Combined with Amazon EC2 for running the application, and Amazon S3 for storing the data, we can run large jobs very economically. This paper describes how to use Amazon Web Services and Hadoop to run an ad hoc analysis on a large collection of web access logs that otherwise would have cost a prohibitive amount in either time or money.
Introduction This document describes how Map and Reduce operations are carried out in Hadoop. If you are not familiar with the Google [WWW] MapReduce programming model you should get acquainted with it first.