Apache Pig offers a variety of Data Processing Operators written in Pig Latin, allowing you to manipulate and transform large datasets. Here's a breakdown of some key operators:
Data Loading and Inspection:
- LOAD: This operator retrieves data from storage systems like HDFS (Hadoop Distributed File System) and converts it into a Pig relation (essentially a data set).
- DUMP: Used to view the contents of a relation on the screen.
- DESCRIBE: Provides information about the schema (structure) of a relation, including column names and data types.
Data Transformation:
- FILTER: Selects specific rows from a relation based on a condition you define.
- FOREACH: Applies transformations to individual rows (tuples) within a relation.
- SPLIT: Splits a data field into multiple fields based on a delimiter (e.g., comma in a CSV file).
Data Aggregation:
- GROUP: Groups data together based on the values in one or more columns.
- ORDER BY: Sorts the relation based on the values in a specified column (ascending or descending order).
- DISTINCT: Eliminates duplicate rows from a relation.
- LIMIT: Restricts the number of output rows returned.
Data Combination:
- JOIN: Combines rows from two or more relations based on a shared field (inner join by default, other join types are also available).
- UNION: Merges the contents of two relations into a single relation.
User-Defined Functions (UDFs):
- DEFINE: Assigns an alias to a UDF (written in Java or other languages) for extending Pig's functionality with custom functions.
These are some of the core data processing operators in Pig. By combining these operators, you can create powerful Pig Latin scripts to process and analyze large datasets.