Course Outline

Module 1: Introduction to Big Data and Hadoop

• Overview of Big Data concepts and challenges
• Introduction to Apache Hadoop ecosystem
• Understanding Hadoop Distributed File System (HDFS)
• Basics of MapReduce and its role in distributed computing
• Case Study: Implementing a basic Big Data solution using Hadoop for a retail business

Module 2: Hadoop Architecture and Ecosystem

• Deep dive into Hadoop architecture and components
• Understanding Hadoop Distributed File System (HDFS) architecture
• Overview of Hadoop ecosystem components: YARN, MapReduce, Hive, Pig, HBase, etc.
• Case Study: Analyzing the scalability of a Hadoop architecture in a large financial organization

Module 3: Setting Up Hadoop Environment

• Installing and configuring Hadoop on a single node and multi-node cluster
• Configuring Hadoop daemons and services
• Hands-on exercises with Hadoop setup and configuration
• Practical Component: Deploying a multi-node Hadoop cluster for a mid-sized enterprise

Module 4: HDFS and MapReduce

• Understanding HDFS architecture and data replication
• Hands-on exercises with HDFS commands and operations
• Writing MapReduce programs in Java for data processing tasks
• Debugging and optimizing MapReduce jobs
• Case Study: Processing and analyzing social media data using HDFS and MapReduce

Module 5: Advanced MapReduce

• Combiners, Partitioners, and InputFormats in MapReduce
• MapReduce join techniques: Map-side and Reduce-side joins
• Working with MultipleOutputs in MapReduce
• Hands-on exercises with advanced MapReduce techniques
• Case Study: Optimizing a large-scale data processing pipeline with advanced MapReduce

Module 6: Introduction to Apache Hive

• Overview of Apache Hive and its architecture
• Working with HiveQL for querying structured data
• Hive data modeling and partitioning
• Hands-on exercises with Hive for data analysis tasks
• Practical Component: Using Hive for data warehousing in an e-commerce company

Module 7: Apache Pig

• Introduction to Apache Pig and its features
• Writing Pig Latin scripts for data processing tasks
• Using Pig for ETL (Extract, Transform, Load) operations
• Hands-on exercises with Apache Pig
• Practical Component: Developing ETL pipelines with Apache Pig for a telecom company

Module 8: Apache HBase

• Introduction to Apache HBase and its architecture
• Data modeling with HBase: Tables, Rows, and Columns
• Performing CRUD (Create, Read, Update, Delete) operations with HBase
• Hands-on exercises with Apache HBase
• Case Study: Implementing real-time data analytics using HBase in a healthcare system

Module 9: Introduction to Apache Spark

• Overview of Apache Spark framework and its advantages
• Spark architecture: RDDs, DataFrames, and Spark SQL
• Writing Spark applications in Scala or Python
• Hands-on exercises with basic Spark operations
• Case Study: Accelerating data processing workflows with Apache Spark in a logistics company

Module 10: Integration with Hadoop Ecosystem

• Running Spark on YARN for resource management
• Integrating Spark with HDFS and Hive
• Performing data processing tasks using Spark with Hadoop ecosystem tools
• Hands-on exercises demonstrating integration with Hadoop ecosystem
• Practical Component: End-to-end big data analytics project integrating Spark with the Hadoop ecosystem