Complete Course of Apache Pig
Apache Pig is a high-level platform built on top of Hadoop that simplifies the process of writing complex MapReduce programs for analyzing large datasets.
It uses Pig Latin, a scripting language designed to handle both structured and unstructured data, making it easier for data analysts to process and transform
large-scale data without writing extensive Java code. Learning Apache Pig is valuable for those pursuing a career in the big data industry,
as it allows professionals to work with Hadoop more efficiently, especially in data transformation, ETL pipelines, and batch processing scenarios.
Chapter 1: Introduction to Big Data and the Big Data Ecosystem
Lesson 1: What is Big Data?
Lesson 2: Distributed Storage & Processing Fundamentals
Lesson 3: Overview of Big Data Tools and Frameworks
Lesson 4: Comparing Apache Pig with Other Tools
Lesson 5: Use Cases and Applications of Apache Pig
Chapter 2: Overview of Apache Pig
Lesson 1: What is Apache Pig?
Lesson 2: History and Evolution of Apache Pig
Lesson 3: Key Features and Benefits
Lesson 4: Apache Pig vs. Traditional MapReduce
Lesson 5: New Features and Enhancements in Recent Releases
Chapter 3: Setting Up Apache Pig
Lesson 1: System Requirements and Prerequisites
Lesson 2: Installing Apache Pig on Local Machines
Lesson 3: Deploying Pig on a Cluster Environment
Lesson 4: IDE Integration for Apache Pig Development
Lesson 5: Running Pig in Different Execution Modes
Chapter 4: Apache Pig Language (Pig Latin) Fundamentals
Lesson 1: Introduction to Pig Latin Syntax and Structure
Lesson 2: Data Types, Schemas, and Operators
Lesson 3: Loading Data with the LOAD Command
Lesson 4: Basic Data Transformations
Lesson 5: Storing Data with the STORE Command
Chapter 5: Intermediate Data Processing with Apache Pig
Lesson 1: Grouping and Aggregation
Lesson 2: Data Joins and Unions
Lesson 3: Sorting and Filtering Data
Lesson 4: Handling Nested Data Structures
Lesson 5: Advanced Transformation Techniques
Chapter 6: Advanced Apache Pig Commands and Features
Lesson 1: Advanced Operators and Constructs
Lesson 2: User Defined Functions (UDFs)
Lesson 3: Parameterization and Macro Functions
Lesson 4: Execution Analysis and Optimization Tools
Lesson 5: Error Handling and Debugging Techniques
Chapter 7: Apache Pig Command-Line Interface and Scripting
Lesson 1: Introduction to the Grunt Shell
Lesson 2: Writing and Running Pig Scripts
Lesson 3: Command-Line Options and Flags
Lesson 4: Debugging via the Command Line
Lesson 5: Automating Pig Workflows with Shell Scripting
Chapter 8: Integrating Apache Pig with the Hadoop Ecosystem
Lesson 1: Interacting with HDFS
Lesson 2: Pig and Hive: Bridging the Gap
Lesson 3: Integrating with HBase and NoSQL Systems
Lesson 4: Alternative Execution Engines: Tez and Spark
Lesson 5: Data Ingestion and Interoperability
Chapter 9: Performance Tuning and Optimization in Apache Pig
Lesson 1: Best Practices for Writing Efficient Pig Scripts
Lesson 2: Execution Plan Analysis with EXPLAIN/ILLUSTRATE
Lesson 3: Tuning Pig Parameters and Resource Management
Lesson 4: Parallel Execution and Load Balancing
Lesson 5: Monitoring and Debugging Performance Issues
Chapter 10: Advanced Topics in Apache Pig
Lesson 1: Developing and Integrating Custom UDFs
Lesson 2: Multi-Language UDF Integration
Lesson 3: Handling Complex Data Structures and Schema Evolution
Lesson 4: Security and Access Control in Pig
Lesson 5: Emerging Features in the Latest Apache Pig Releases
Chapter 11: Real-World Applications and Case Studies
Lesson 1: Apache Pig in ETL and Data Warehousing
Lesson 2: Log Analysis and Processing
Lesson 3: Social Media Data Analytics
Lesson 4: Financial and Transactional Data Processing
Lesson 5: Lessons Learned from Production Deployments
Chapter 12: Administration, Maintenance, and Best Practices
Lesson 1: Managing Pig Scripts in a Multi-User Environment
Lesson 2: Monitoring and Logging for Apache Pig
Lesson 3: Troubleshooting Common Issues and Debugging Strategies
Lesson 4: Upgrading and Migrating Apache Pig Installations
Lesson 5: Best Practices for Cluster Management with Pig Workloads
Chapter 13: Apache Pig in Advanced Big Data Analytics
Lesson 1: Integrating Pig with Machine Learning Workflows
Lesson 2: Advanced Data Visualization and Reporting
Lesson 3: Real-Time Data Processing and Streaming Analytics
Lesson 4: Case Studies in Advanced Analytics Applications
Lesson 5: Future Directions in Big Data Analytics with Apache Pig
Chapter 14: Capstone Project and Course Wrap-Up
Lesson 1: Designing a Comprehensive Apache Pig Data Pipeline
Lesson 2: Implementation: Building and Testing Your Pig Scripts
Lesson 3: Performance Optimization and Debugging in Your Project
Lesson 4: Project Presentation and Peer Review
Lesson 5: Course Summary, Key Takeaways, and Next Steps
