Map Reduce Training
Introduction to MapReduce
Map Reduce is a programming model used for processing large data sets with a distributed algorithm on a cluster. In this introductory module, you'll learn the fundamentals of MapReduce, including its architecture, components, and how it enables efficient data processing across a distributed system.
Understanding the Map and Reduce Functions
Dive into the core components of the MapReduce model: the Map and Reduce functions. Learn how the Map function processes input data into key-value pairs, and how the Reduce function aggregates these pairs to generate the final output. Understand how these functions work together to solve large-scale data processing problems.
Data Partitioning and Shuffling
Data partitioning and shuffling are critical steps in the MapReduce workflow. Learn how data is split across different nodes in a cluster, and how the shuffling process ensures that data is correctly distributed to the appropriate reducers. Explore techniques to optimize data partitioning for better performance.
Implementing MapReduce in Hadoop
Learn how to implement MapReduce using the Hadoop framework. Understand the Hadoop Distributed File System (HDFS) and how it interacts with MapReduce. Explore the Hadoop ecosystem, including tools like Pig and Hive, to simplify and optimize your MapReduce workflows.
Advanced Map Reduce Techniques
Delve into advanced MapReduce techniques to enhance the efficiency and scalability of your data processing tasks. Learn about combiners, secondary sorting, and how to optimize performance by tuning various parameters. Explore the use of MapReduce in complex applications such as graph processing and machine learning.
Data Processing with YARN
Discover how to use YARN (Yet Another Resource Negotiator) to manage resources in a Hadoop cluster. Learn how YARN schedules and monitors jobs, and how it enhances the scalability and flexibility of your MapReduce applications.
Map Reduce for Big Data Analytics
Learn how to apply Map Reduce in big data analytics. Explore case studies and real-world applications where MapReduce is used to process massive data sets, including log analysis, data mining, and ETL (Extract, Transform, Load) processes. Understand how MapReduce integrates with other big data tools and platforms.
Testing and Debugging Map Reduce Jobs
Gain insights into best practices for testing and debugging MapReduce jobs. Learn how to use various tools to test the functionality and performance of your MapReduce code. Explore common issues that arise during job execution and how to troubleshoot them effectively.
MapReduce Syllabus
Introduction to Distributed Computing and Big Data
- Overview of Distributed Computing: Concepts, challenges, and benefits
- Introduction to Big Data: Characteristics, sources, and applications
- Challenges in Big Data Processing: Volume, velocity, variety, and veracity
Introduction to MapReduce
- Evolution of MapReduce: Origins and development
- MapReduce Programming Model: Map and Reduce phases
- Advantages and Limitations of MapReduce
Hadoop Ecosystem Overview
- Introduction to Hadoop: Architecture and components (HDFS, YARN)
- Hadoop Distributed File System (HDFS): Storage and data replication
- Resource Management with YARN: Job scheduling and execution
Setting Up a Hadoop Cluster
- Installing Hadoop: Single-node and multi-node cluster setup
- Configuring Hadoop: XML configuration files and parameters
- Hadoop Cluster Management: Monitoring and administration tools
MapReduce Basics
- Anatomy of a MapReduce Job: Mapper, Reducer, InputFormat, OutputFormat
- MapReduce Execution Flow: Job submission and execution steps
- Writing and Running a MapReduce Program: Example walkthrough
MapReduce Advanced Concepts
- Combiner and Partitioner: Optimization techniques in MapReduce
- Distributed Cache: Sharing files across MapReduce tasks
- Input and Output Formats: Handling different data formats (Text, SequenceFile, Avro)
MapReduce Design Patterns
- MapReduce Design Patterns: Common patterns (Filtering, Summarization, Joins)
- Optimization Techniques: Data locality, speculative execution, task tuning
- Advanced MapReduce Patterns: Secondary sort, count distinct, inverted index
Handling Large-scale Data with MapReduce
- Scaling MapReduce: Handling large datasets with partitioning and shuffling
- Performance Tuning: Optimization strategies for MapReduce jobs
MapReduce Algorithms and Applications
- PageRank Algorithm: Implementing PageRank using MapReduce
- Word Count Example: Variations and optimizations
- Graph Processing: Graph algorithms using MapReduce (BFS, DFS)
Integration with Other Big Data Technologies
- Integration with Hive: Using HiveQL for data analysis
- Integration with Pig: Using Pig Latin for data processing
- Integration with Spark: Comparing MapReduce and Spark
Real-time Big Data Processing
- Overview of Real-time Processing: Stream processing vs. batch processing
- Apache Kafka Integration: Using Kafka with Hadoop ecosystem
- Processing Streaming Data: Using tools like Apache Storm or Apache Flink
MapReduce Project Work and Case Studies
- Real-world MapReduce Projects: Implementation and evaluation
- Case Studies: Industry-specific applications (e.g., retail, healthcare)
- Presentation and Documentation of MapReduce Projects
Training
Basic Level Training
Duration : 1 Month
Advanced Level Training
Duration : 1 Month
Project Level Training
Duration : 1 Month
Total Training Period
Duration : 3 Months
Course Mode :
Available Online / Offline
Course Fees :
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