Apache Pig
1. Overview
1.1. Definition and Purpose
- Apache Pig is a high-level platform for creating programs that run on Apache Hadoop.
- It is designed to process large data sets using a programming language known as Pig Latin.
1.2. Components
- Pig Latin: A scripting language for expressing data flows. It is easier to write compared to Java MapReduce code.
- Pig Execution Environment: Where Pig Latin scripts are executed, usually on a cluster of machines.
- Pig Compiler: Converts Pig Latin into sequences of MapReduce jobs for execution on Hadoop.
1.3. Key Features
- Ease of Use: Pig Latin is simpler than Java MapReduce for developers to write and understand.
- Extensibility: Users can create User-Defined Functions (UDFs) to execute custom processing logic.
- Optimization Opportunities: The Pig framework optimizes execution plans automatically.
1.4. Applications
- Used in sectors like research analytics, log data processing, and data pipeline construction.
- Typically employed in scenarios where large volumes of data need a batch processing model.
1.5. Comparison to Other Tools
- Unlike Hive, which is more SQL-oriented, Pig is more procedural and script-based.
- It offers flexibility and efficiency for certain types of data transformations.
1.6. Integration
- Works seamlessly with Hadoop's ecosystem including HDFS for storage and MapReduce for processing.
- Can be integrated with other tools like Apache Oozie for workflow scheduling.
Tags::tool:data: