| # Licensed to the Apache Software Foundation (ASF) under one or more |
| # contributor license agreements. See the NOTICE file distributed with |
| # this work for additional information regarding copyright ownership. |
| # The ASF licenses this file to You under the Apache License, Version 2.0 |
| # (the "License"); you may not use this file except in compliance with |
| # the License. You may obtain a copy of the License at |
| # |
| # http://www.apache.org/licenses/LICENSE-2.0 |
| # |
| # Unless required by applicable law or agreed to in writing, software |
| # distributed under the License is distributed on an "AS IS" BASIS, |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| # See the License for the specific language governing permissions and |
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| |
| Source: pig |
| Section: misc |
| Priority: extra |
| Maintainer: Bigtop <dev@bigtop.apache.org> |
| Build-Depends: debhelper (>= 7.0.50~) |
| Standards-Version: 3.8.0 |
| Homepage: http://pig.apache.org/ |
| |
| Package: pig |
| Architecture: all |
| Depends: hadoop-client, hbase, hive, zookeeper, bigtop-utils (>= 0.7) |
| Description: Pig is a platform for analyzing large data sets |
| 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 (e.g., the Hadoop subproject). 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. Complex tasks comprised of multiple interrelated data transformations |
| are explicitly encoded as data flow sequences, making them easy to write, understand, |
| and maintain. |
| * Optimization opportunities |
| The way in which tasks are encoded permits the system to optimize their execution |
| automatically, allowing the user to focus on semantics rather than efficiency. |
| * Extensibility |
| Users can create their own functions to do special-purpose processing. |
