Introduction to MapReduce

• Overview of distributed computing and MapReduce paradigm
• Understanding the motivation behind MapReduce
• Comparison of MapReduce with traditional computing models

MapReduce Architecture

• Understanding the architecture of a MapReduce system
• Roles of master and worker nodes in a MapReduce cluster
• Communication and coordination between nodes

MapReduce Programming Model

• Introduction to the MapReduce programming model
• Map function and its role in data processing
• Reduce function and data aggregation

MapReduce Algorithms and Patterns

• Exploring common MapReduce algorithms and patterns
• Word count, sorting, filtering, and grouping
• Join operations and data transformation

MapReduce Data Flow and Shuffling

• Understanding the data flow in a MapReduce job
• Shuffling and sorting of intermediate key-value pairs
• Reducing network transfer and optimizing data locality

Input and Output Formats

• Handling different input formats (text, CSV, JSON, etc.)
• Output formats and writing results to different storage systems
• Customizing input and output formats in MapReduce jobs

Handling Large-scale Data

• Partitioning and splitting data in MapReduce
• Combiners and partial aggregation
• Techniques for handling large datasets

Performance Optimization in MapReduce

• Performance considerations and bottlenecks in MapReduce
• Optimizing map and reduce tasks
• Tuning memory settings and task parallelism

Fault Tolerance and Error Handling

• Understanding fault tolerance in MapReduce
• Task re-execution and job recovery
• Handling errors and failures in MapReduce jobs

MapReduce Frameworks and Ecosystem

• Overview of popular MapReduce frameworks (Hadoop, Apache Spark, etc.)
• Comparison of different frameworks and their features
• Integration of MapReduce with other big data technologies

Advanced MapReduce Concepts

• MapReduce combiners and partitioners
• Counters and distributed cache
• Chaining MapReduce jobs and job dependencies

MapReduce Design Patterns

• Exploring common design patterns in MapReduce
• Secondary sort and order inversion
• Multi-step and multi-input jobs

MapReduce for Big Data Analytics

• Using MapReduce for data analysis and machine learning
• Implementing statistical calculations and algorithms
• MapReduce for graph processing and social network analysis

MapReduce Best Practices and Optimization

• Best practices for designing efficient MapReduce jobs
• Optimizing input/output operations and data processing
• Troubleshooting and debugging MapReduce jobs