Apache Pig Basics | Pig Architecture | Pig Execution | Pig Data Models

Apache 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. 0:00 Intro 0:26 What is Pig 4:33 Why Pig Needed 8:40 Features of Pig 11:31 History 12:26 When to use Pig 13:11 When not to use Pig 14:56 Applications 19:48 Yahoo and Pig 23:58 Pig vs MapReduce 26:19 Pig vs SQL 28:36 Pig vs Hive 30:08 Pig Architecture 32:40 Pig Execution 45:35 Data Models