Data Processing Operators in Apache Pig.pptx

INTRODUCTION TO DATA PROCESSING OPERATORS IN APACHE PIG

1. Big Data Analytics: DATA PROCESSING OPERATORS IN APACHE PIG Name: ASIF MK Reg No: 24MBADAPY0043

2. Introduction Apache Pig is a high-level platform for analyzing large datasets. Uses Pig Latin, a data flow language, for transformations. It runs on Hadoop and simplifies big data processing. Provides operators to turn raw data into meaningful insights. Data processing operators in Apache Pig are commands that transform, filter, combine, and analyze large datasets in a simple, SQL-like way.

3. Loading and Storing Data Loading and Storing operators in Pig are used to import raw data for processing and export the results after analysis. LOAD operator is used to read data from HDFS or local file system into Pig. Data can be loaded using different functions like PigStorage(), TextLoader(), or custom loaders. Schema (fields & types) can be defined while loading for structured processing. STORE operator is used to write processed results back to HDFS or local storage. Output can be stored in various formats such as CSV, plain text, or custom storage functions. 1 3 2 4 5

4. Relational Operators Relational operators in Apache Pig are used to transform, filter, group, and combine data just like SQL operations. Select specific rows based on conditions. Project or transform columns. Organize data by key(s). FILTER FOREACH … GENERATE GROUP / COGROUP Combine multiple relations. JOIN / UNION Sort data or remove duplicates. ORDER / DISTINCT

5. Other Data Operators Groups multiple relations by a common key. Merges two or more datasets into one. Produces a Cartesian product of datasets. Divides data into subsets based on conditions. Retrieves only the first N records. COGROUP UNION CROSS SPLIT LIMIT Other Data Operators SAMPLE Extracts a random subset of data.

6. Functions in Pig Functions in Apache Pig provide built-in and custom operations to transform, aggregate, and manage data efficiently. Operate on individual records to transform or compute values (e.g., UPPER(), ROUND()). Custom functions written in Java, Python, etc., to extend Pig’s functionality. Ready-made functions provided by Pig for common tasks like math, string, and date operations. ummarize grouped data into a single value (e.g., SUM(), AVG(), COUNT()). Eval Functions User Defined Functions (UDFs) built-in functions Aggregation Functions

7. Example Workflow Example Workflow in Apache Pig 04 Apply aggregation 02 Filter records 01 Load the data 05 Store the result 03 Group data students = LOAD 'students.txt' USING PigStorage(',') AS (id:int, name:chararray, age:int, marks:int); adults = FILTER students BY age >= 18; grouped = GROUP adults BY age; avg_marks = FOREACH grouped GENERATE group, AVG(adults.marks); STORE avg_marks INTO 'output' USING PigStorage(','); Output Workflow

8. Conclusion Apache Pig simplifies the processing of large datasets by offering high-level operators and functions that work seamlessly with Hadoop. Through its data flow language, Pig Latin, it provides users with intuitive tools to load, transform, combine, and analyze data efficiently. The wide range of relational, combination, and functional operators makes it flexible for both simple queries and complex workflows. By abstracting the underlying MapReduce tasks, Pig empowers business analysts and data professionals to focus on insights rather than low-level coding, making it a powerful tool in the big data ecosystem.