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Fundamentals of data science

1. Introduction to Pig
Pig is a high-level platform for analyzing large datasets. It uses a language called Pig Latin that simplifies data manipulation and
analysis tasks.
Pig definition
Pig is a high-level platform for analyzing large datasets. It is designed for data analysts and developers who need to perform complex
data transformations and analysis tasks.

2. Pig Anatomy
Data Flow
Data flows through Pig in a series of
operations, starting from the source and
ending at the output.
Load
Transform
Store
Operators
Pig uses a wide range of operators for
different operations, like filtering,
grouping, and joining data.
Filter
Group
Join
Scripts
Pig scripts define data flow and
transformations using Pig Latin syntax.
Load data
Define transformations
Store results

3. Pig on Hadoop
Distributed Processing
Pig leverages Hadoop's distributed
computing capabilities for parallel
processing of massive datasets.
Scalability
It can handle data volumes that
exceed the capacity of a single
machine, making it suitable for big
data analysis.
Efficiency
Pig optimizes data flow and execution,
enabling faster and more efficient data
processing.

4. Pig Philosophy
1
Simplicity
Pig Latin syntax is easy to learn and
use, making it accessible to users
with varied technical backgrounds.
2
Expressiveness
The language provides rich
operators and constructs to support
diverse data analysis tasks.
3
Extensibility
Pig allows users to write custom
functions and extend its capabilities
to handle specific needs.
4
Performance
Pig optimizes data flow and execution, resulting in efficient and scalable data processing.

5. ETL Processing with Pig
1
Extract
Data is extracted from various sources,
such as databases, files, or APIs.
2
Transform
Data is cleaned, transformed, and
prepared for analysis, using Pig Latin
operators.
3
Load
The transformed data is loaded into a
data warehouse or other destination for
further analysis.

6. Pig Latin Overview
LOAD Reads data from a source.
FILTER Selects specific data based on conditions.
GROUP Combines data based on a key.
JOIN Combines data from multiple datasets.
FOREACH Applies operations to individual data elements.
STORE Writes the processed data to a destination.