Description:

This comprehensive three-day course equips data professionals with practical expertise in Apache Hadoop and Apache Spark, the foundational technologies for large-scale distributed data processing. Participants will learn the architecture and design principles behind Hadoop's storage (HDFS) and processing (MapReduce) layers, explore the Spark ecosystem's advantages over traditional batch processing, and gain hands-on experience with real-world big data scenarios. The course bridges the gap between foundational concepts and applied problem-solving in modern data engineering.

Duration: 

3 Days

Course Code: BDT30

Learning Objectives:

After this course, you will be able to:

1. Understand the five V's of Big Data and core Hadoop architecture principles (parallel execution, data locality, fault tolerance)
2. Design and implement distributed storage solutions using HDFS and master-slave cluster topologies
3. Compare MapReduce and Apache Spark processing models, and apply Spark transformations, actions, and RDDs to solve real-world data problems
4. Evaluate Hadoop distributions (Cloudera, Hortonworks, MapR) and cloud deployment options (Amazon EMR, Google Dataproc, Azure HDInsight)

1. Data Engineers and Software Engineers looking to build expertise in distributed data processing
2. Data Scientists and Analysts seeking to understand big data infrastructure and optimization
3. IT Professionals and Solutions Architects evaluating big data platform deployments

1. Familiarity with command-line interfaces and basic Linux/Unix commands
2. Understanding of core data structures and basic programming concepts
3. Optional: Prior experience with Python, Java, or Scala is beneficial

Course Outline:

Day 1: Big Data Fundamentals & Hadoop Architecture

Module 1: Big Data Concepts & Hadoop Introduction

1. The Five V's of Big Data (Volume, Velocity, Variety, Veracity, Value)
2. What is Hadoop: Open source framework, scalability, fault tolerance, and economic benefits
3. Hadoop ecosystem overview: Storage, Processing, Administration, and Data Ingestion layers
4. Hadoop creation history and evolution of the technology

Module 2: Hadoop Architecture & HDFS Deep Dive

1. Hadoop Secret Sauce: Parallel Execution, Data Locality, and Fault Tolerance
2. HDFS architecture: NameNode, DataNodes, and replication strategy
3. Master-Slave cluster topology and distributed file system concepts
4. Hands-on: Exploring HDFS file operations and data placement

Day 2: MapReduce Processing & Spark Fundamentals

Module 3: MapReduce Programming Model

1. MapReduce paradigm: Mapper, Reducer, and Driver components
2. Word count and real-world use case implementations
3. Job submission, task scheduling, and performance optimization
4. Limitations of MapReduce and motivation for Spark

Module 4: Apache Spark Introduction & RDDs

1. Spark history and evolution as a unified analytics platform
2. Resilient Distributed Datasets (RDDs): Immutability, lineage, and lazy evaluation
3. Transformations vs. Actions and the Spark execution model
4. Spark libraries overview: SQL, Streaming, MLlib, GraphX, and Deep Learning

Day 3: Advanced Spark & Big Data Ecosystems

Module 5: Spark Programming & Optimization

1. Spark word count: Scala, Python, and Java implementations
2. Working with DataFrames and Spark SQL for structured data
3. Caching, persistence, and performance tuning strategies
4. Hands-on labs: Data transformation pipelines and analytical queries

Module 6: Big Data Distributions & Deployment

1. Hadoop distributions: Cloudera, Hortonworks, MapR, Databricks—features and differentiation
2. Cloud deployment options: Amazon EMR, Google Dataproc, Microsoft Azure HDInsight
3. Unified Analytics Platform: Databricks ecosystem and Spark deployment patterns
4. Best practices, next steps, and hands-on project guidance

Each day includes multiple interactive demonstrations, hands-on exercises using Databricks notebooks, and practical case studies from real-world big data scenarios.