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Apache Hadoop Mastery

Course Level: Intermediate to Advanced Prerequisites: Java basics, Linux command line, basic distributed systems concepts Duration: 25-30 hours Hands-on Projects: 15+ coding exercises and real-world scenarios

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What You’ll Master

Apache Hadoop revolutionized big data processing by making distributed computing accessible and reliable. This comprehensive course takes you from foundational concepts to production-ready implementations, following the same architectural principles outlined in the original Google papers (GFS and MapReduce) that inspired Hadoop’s design. You’ll gain deep expertise in:
  • Distributed Storage: HDFS architecture, replication strategies, and fault tolerance mechanisms
  • Parallel Processing: MapReduce programming model and optimization techniques
  • Resource Management: YARN architecture and cluster resource orchestration
  • Ecosystem Integration: Hive, Pig, HBase, and streaming frameworks
  • Production Operations: Deployment, monitoring, tuning, and troubleshooting at scale
This course emphasizes both theoretical foundations (based on seminal distributed systems papers) and practical implementation (production-ready code examples).

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Course Structure

Duration: 3-4 hours | Foundation ModuleStart with the theoretical foundations that inspired Hadoop’s design. This module breaks down the seminal Google File System (GFS) and MapReduce papers in an accessible way, connecting academic concepts to Hadoop’s practical implementation.What You’ll Learn:
  • The distributed systems challenges that necessitated Hadoop
  • Deep dive into the Google File System (GFS) paper - simplified
  • Understanding the MapReduce programming model from the original paper
  • How Hadoop translates research concepts into production systems
  • Evolution from Hadoop 1.x to modern 3.x architecture
Key Topics:
  • GFS design principles: component failures as the norm, large files, append-mostly workloads
  • MapReduce abstraction: hiding parallelization complexity from developers
  • CAP theorem implications for distributed file systems
  • Hadoop’s architectural decisions and trade-offs
Start Learning →
Duration: 4-5 hours | Core ModuleMaster the Hadoop Distributed File System - the storage backbone of the entire Hadoop ecosystem. Learn how HDFS achieves fault tolerance, scalability, and high throughput for large datasets.What You’ll Learn:
  • HDFS architecture: NameNode, DataNode, and Secondary NameNode
  • Block storage and replication strategies
  • Rack awareness and data locality optimization
  • Read/write data flow and fault tolerance mechanisms
  • HDFS Federation and High Availability (HA)
Hands-on Labs:
  • Setting up a multi-node HDFS cluster
  • Configuring replication factors and block sizes
  • Implementing custom block placement policies
  • Simulating node failures and observing recovery
  • Using HDFS CLI and Java API
Code Examples:
  • Java HDFS client for file operations
  • Monitoring HDFS health programmatically
  • Custom InputFormat for optimized reading
Deep Dive →
Duration: 5-6 hours | Core ModuleLearn to design and implement MapReduce jobs for large-scale data processing. Understand the execution framework, data flow, and optimization techniques.What You’ll Learn:
  • MapReduce programming paradigm: Map, Shuffle, Reduce phases
  • Job execution flow: JobTracker, TaskTracker coordination
  • Partitioning, sorting, and combining strategies
  • Custom data types with Writable interface
  • Advanced patterns: joins, secondary sorting, chain jobs
Hands-on Projects:
  • Classic WordCount with variations
  • Log analysis and aggregation
  • Distributed grep and text processing
  • Implementing relational joins in MapReduce
  • Time-series data analysis
Code Coverage:
  • Mapper and Reducer implementations
  • Combiner optimization techniques
  • Custom partitioners for load balancing
  • Unit testing MapReduce jobs with MRUnit
Start Coding →
Duration: 3-4 hours | Core ModuleExplore YARN (Yet Another Resource Negotiator) - Hadoop 2.x’s revolutionary resource management layer that decouples resource management from programming model.What You’ll Learn:
  • YARN architecture: ResourceManager, NodeManager, ApplicationMaster
  • Resource allocation and scheduling (FIFO, Fair, Capacity schedulers)
  • Container-based execution model
  • Application lifecycle and fault tolerance
  • Running non-MapReduce applications on YARN (Spark, Flink)
Practical Skills:
  • Configuring scheduler policies for multi-tenant clusters
  • Resource queue management and priority allocation
  • Monitoring YARN applications and resource utilization
  • Writing custom YARN applications
  • Troubleshooting common YARN issues
Master YARN →
Duration: 4-5 hours | Integration ModuleNavigate the rich Hadoop ecosystem and learn how to integrate various tools for comprehensive data solutions.What You’ll Learn:
  • Hive: SQL-on-Hadoop for data warehousing
  • Pig: Data flow scripting for ETL pipelines
  • HBase: Distributed NoSQL database on HDFS
  • Sqoop: Importing/exporting data from RDBMS
  • Flume: Log aggregation and streaming ingestion
  • Oozie: Workflow scheduling and coordination
Integration Patterns:
  • Building end-to-end data pipelines
  • Choosing the right tool for specific use cases
  • Combining batch and interactive processing
  • Data governance with Apache Atlas
Code Examples:
  • HiveQL queries for complex analytics
  • Pig Latin scripts for data transformation
  • HBase Java API for real-time access
  • Oozie workflow XML configurations
Explore Ecosystem →
Duration: 4-5 hours | Advanced ModuleLearn proven patterns for efficient data processing and discover anti-patterns to avoid in production environments.What You’ll Learn:
  • Design patterns: filtering, summarization, joins, organization
  • Performance optimization techniques
  • Memory management and tuning parameters
  • Compression strategies and codec selection
  • Data serialization formats (Avro, Parquet, ORC)
Real-World Scenarios:
  • Processing clickstream data at scale
  • Log analysis and anomaly detection
  • Building recommendation systems
  • Graph processing with Hadoop
  • Machine learning pipelines
Code Deep-Dives:
  • Implementing reduce-side joins efficiently
  • Using counters for job metrics and monitoring
  • Custom RecordReader for complex input formats
  • Chaining multiple MapReduce jobs
  • Implementing distributed cache for lookup data
Advanced Patterns →
Duration: 3-4 hours | Operations ModuleDeploy, monitor, and maintain production-grade Hadoop clusters. Learn operational best practices and troubleshooting strategies.What You’ll Learn:
  • Cluster planning: hardware selection and sizing
  • Security: Kerberos authentication, authorization with Ranger
  • Monitoring and alerting with Ambari, Cloudera Manager
  • Backup and disaster recovery strategies
  • Capacity planning and cluster growth management
  • Common failure scenarios and remediation
Operational Skills:
  • Setting up Hadoop cluster on cloud (AWS, Azure, GCP)
  • Configuring security policies and encryption
  • Performance tuning for specific workloads
  • Troubleshooting slow jobs and cluster bottlenecks
  • Upgrading Hadoop versions with zero downtime
Best Practices:
  • Network topology considerations
  • Data lifecycle management
  • Cost optimization strategies
  • SLA management and monitoring
Deploy to Production →
Duration: 4-5 hours | Hands-on ProjectApply everything you’ve learned by building a production-ready, end-to-end data processing pipeline.Project Overview: Build a real-time log analytics platform that ingests, processes, and analyzes web server logs to generate business insights.Components You’ll Build:
  • Data ingestion layer with Flume
  • HDFS storage with optimized partitioning
  • MapReduce jobs for sessionization and aggregation
  • Hive tables for interactive analysis
  • HBase for real-time lookups
  • Oozie workflows for orchestration
  • Dashboard with visualization
Skills Demonstrated:
  • End-to-end architecture design
  • Performance optimization
  • Error handling and monitoring
  • Testing and validation
Start Building →

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Learning Path

1

Foundation

Begin with the research papers that inspired Hadoop. Understanding the why behind architectural decisions makes learning the how much easier.Start with Papers →
2

Core Components

Master HDFS, MapReduce, and YARN - the three pillars of Hadoop. These modules build on each other sequentially.Modules 2-4 | 12-15 hours
3

Ecosystem & Integration

Expand your toolkit with ecosystem components and learn when to use each tool.Module 5 | 4-5 hours
4

Advanced & Production

Apply advanced patterns and learn operational best practices for production environments.Modules 6-7 | 7-9 hours
5

Capstone

Build a complete data pipeline to solidify your learning and create a portfolio project.Final Project | 4-5 hours

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Why Learn Hadoop?

Industry Standard

Hadoop remains the foundation of enterprise big data infrastructure, with widespread adoption across Fortune 500 companies.

Career Opportunities

Hadoop skills are highly valued, with data engineers commanding competitive salaries and abundant job opportunities.

Ecosystem Gateway

Understanding Hadoop opens doors to modern frameworks like Spark, Flink, and cloud-native data platforms.

Distributed Systems Mastery

Hadoop teaches fundamental distributed systems concepts applicable to any large-scale system design.

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What Makes This Course Different?

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1. Research Paper Foundation

Unlike typical tutorials, we start with the seminal papers (GFS, MapReduce) that inspired Hadoop, explained in an accessible way. This gives you deep conceptual understanding, not just surface-level knowledge.

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2. Production-Ready Code

Every code example is production-quality with:
  • Proper error handling
  • Performance optimization
  • Testing strategies
  • Real-world considerations

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3. Operational Focus

We don’t just teach you to write MapReduce jobs - we teach you to deploy, monitor, and maintain production clusters.

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4. Modern Context

Learn how Hadoop fits into the modern data ecosystem alongside Spark, Kafka, cloud data warehouses, and containerized deployments.

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Prerequisites

Before starting this course, you should have:
  • Java Programming: Comfortable with Java 8+ (most Hadoop code is Java-based)
  • Linux Command Line: Basic shell scripting and file system navigation
  • Distributed Systems Basics: Understanding of client-server architecture, basic networking
  • SQL Knowledge: Helpful for Hive module (not strictly required)
If you need to strengthen Java skills, check out our Java Crash Course first.

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Learning Resources

Throughout this course, we reference:
  • Research Papers:
    • “The Google File System” (Ghemawat et al., 2003)
    • “MapReduce: Simplified Data Processing on Large Clusters” (Dean & Ghemawat, 2004)
    • “The Hadoop Distributed File System” (Shvachko et al., 2010)
  • Official Documentation: Apache Hadoop 3.x docs
  • Books: “Hadoop: The Definitive Guide” by Tom White (supplementary)
  • Code Repository: All examples available on GitHub

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Community & Support

Join thousands of engineers mastering distributed systems. Share your progress, ask questions, and collaborate on the capstone project.

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Ready to Begin?

Start your journey into distributed computing with solid theoretical foundations.

Module 1: Introduction & Foundational Papers

Begin with the research that revolutionized big data processing

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Course Outcomes

By completing this course, you’ll be able to:
  • Design and implement scalable MapReduce applications
  • Deploy and manage production Hadoop clusters
  • Optimize data processing jobs for performance and cost
  • Choose appropriate tools from the Hadoop ecosystem
  • Troubleshoot common issues and bottlenecks
  • Architect end-to-end big data solutions
  • Interview confidently for data engineering roles
Estimated Time to Complete: 25-30 hours of focused learning Recommended Pace: 1-2 modules per week for thorough understanding