Complete Course of Apache Hadoop
Apache Hadoop is an open-source framework designed for distributed storage and processing of large datasets across computer clusters using a simple programming model.
It provides scalability, fault tolerance, and flexibility, making it a cornerstone of the big data industry. Learning Hadoop is crucial for aspiring
big data professionals because it is widely adopted by companies to manage and analyze massive amounts of data, making Hadoop expertise a highly sought-after
skill in the job market.
Chapter 1: Introduction to Distributed Storage and Big Data Processing
Lesson 1: What is Distributed Storage?
Lesson 2: Basics of Big Data and Its Challenges
Lesson 3: Introduction to Distributed Processing
Lesson 4: Overview of Tools for Distributed Storage (e.g., HDFS, Ceph, GlusterFS)
Lesson 5: Tools for Distributed Processing (e.g., Apache Spark, Flink, Storm)
Lesson 6: Comparing Apache Hadoop with Other Tools
Chapter 2: Introduction to Apache Hadoop
Lesson 1: What is Apache Hadoop and Its Importance in Big Data?
Lesson 2: History and Evolution of Apache Hadoop
Lesson 3: Key Features of Apache Hadoop
Lesson 4: Components of the Hadoop Ecosystem
Lesson 5: Apache Hadoop Use Cases in Real-World Applications
Chapter 3: Setting Up Apache Hadoop
Lesson 1: System Requirements and Prerequisites for Hadoop
Lesson 2: Installing Hadoop on Local and Cluster Environments
Lesson 3: Configuring Hadoop (Single Node vs. Multi-Node Setup)
Lesson 4: Setting Up Hadoop on Cloud Platforms (AWS, Azure, GCP)
Lesson 5: IDE Integration for Hadoop Development (Eclipse, IntelliJ)
Chapter 4: Hadoop Distributed File System (HDFS)
Lesson 1: Introduction to HDFS and Its Architecture
Lesson 2: HDFS Components (NameNode, DataNode, Secondary NameNode)
Lesson 3: Reading and Writing Data in HDFS
Lesson 4: Data Replication and Fault Tolerance
Lesson 5: HDFS Commands and Examples
Lesson 6: Managing Files and Directories in HDFS
Chapter 5: Hadoop MapReduce
Lesson 1: Introduction to MapReduce Programming Model
Lesson 2: Key Components of MapReduce (Mapper, Reducer, JobTracker, TaskTracker)
Lesson 3: Writing and Running a MapReduce Program
Lesson 4: Data Flow in MapReduce Jobs
Lesson 5: Advanced Concepts in MapReduce (Partitioners, Combiners)
Lesson 6: MapReduce Commands and Optimization Techniques
Chapter 6: Hadoop YARN
Lesson 1: Introduction to YARN (Yet Another Resource Negotiator)
Lesson 2: YARN Architecture and Its Components
Lesson 3: YARN Resource Management and Scheduling
Lesson 4: Running Applications on YARN
Lesson 5: Advanced YARN Commands and Features
Chapter 7: Hadoop Ecosystem Tools
Lesson 1: Overview of Hive (Data Warehousing on Hadoop)
Lesson 2: Introduction to Apache Pig (Data Transformation)
Lesson 3: Basics of HBase (NoSQL Database)
Lesson 4: Apache Zookeeper for Coordination in Hadoop
Lesson 5: Integrating Apache Sqoop and Flume for Data Ingestion
Lesson 6: Apache Oozie for Workflow Scheduling
Chapter 8: Advanced Hadoop Operations
Lesson 1: Hadoop Cluster Setup and Configuration
Lesson 2: Managing and Monitoring Hadoop Clusters
Lesson 3: Hadoop Security and Authentication (Kerberos)
Lesson 4: Hadoop High Availability Setup
Lesson 5: Hadoop Cluster Troubleshooting and Debugging
Chapter 9: Performance Optimization in Hadoop
Lesson 1: Optimizing HDFS Performance
Lesson 2: Tuning MapReduce Jobs
Lesson 3: Resource Optimization in YARN
Lesson 4: Performance Monitoring and Benchmarking Tools
Lesson 5: Best Practices for Hadoop Performance
Chapter 10: Hadoop Integration with Big Data Tools
Lesson 1: Using Hadoop with Apache Spark
Lesson 2: Integrating Hadoop with Kafka for Real-Time Processing
Lesson 3: Hadoop and Machine Learning with Mahout
Lesson 4: Combining Hadoop with ElasticSearch for Search Applications
Lesson 5: Hadoop in Modern Data Pipelines
Chapter 11: Hadoop Common
Lesson 1: Introduction to Hadoop Common
Lesson 2: Core Utilities and Libraries of Hadoop Common
Lesson 3: How Hadoop Common Supports Other Hadoop Components
Lesson 4: Configuration Files in Hadoop Common (core-site.xml, hadoop-env.sh, etc.)
Lesson 5: Common Exceptions and Troubleshooting in Hadoop Common
Lesson 6: Best Practices for Managing Hadoop Common
Chapter 12: Role-Based Access Control (RBAC) in Hadoop
Lesson 1: Introduction to RBAC Systems and Their Importance
Lesson 2: Implementing Role-Based Access Control in Hadoop
Lesson 3: Managing User Roles and Permissions in Hadoop Ecosystem
Lesson 4: Securing Hadoop Components Using RBAC (HDFS, YARN, Hive, etc.)
Lesson 5: Integration of Hadoop RBAC with LDAP and Kerberos
Lesson 6: Monitoring and Auditing Role-Based Access in Hadoop
Lesson 7: Challenges and Solutions in RBAC Implementation for Hadoop
Chapter 13: Introduction to Apache Hudi
Lesson 1: What is Apache Hudi? Overview and Use Cases
Lesson 2: Evolution of Apache Hudi and Features in Version 1.0
Lesson 3: Key Concepts of Apache Hudi (Datasets, Tables, Commit Timeline, etc.)
Lesson 4: Hudi's Table Types: Copy-on-Write (COW) vs Merge-on-Read (MOR)
Lesson 5: Hudi's Architecture and Internal Components
Chapter 14: Apache Hudi and Its Integration with Hadoop
Lesson 1: How Apache Hudi Leverages Hadoop for Distributed Storage and Processing
Lesson 2: Setting Up Apache Hudi in a Hadoop Ecosystem (HDFS, YARN, Hive Integration)
Lesson 3: Apache Hudi's Role in Optimizing Hadoop Workflows
Lesson 4: Real-Time Updates and Querying Data with Hudi on Hadoop
Lesson 5: Comparing Apache Hudi to Other Hadoop Ecosystem Tools (Apache Iceberg, Delta Lake)
Chapter 15: Advanced Features of Apache Hudi 1.0
Lesson 1: Incremental Queries with Apache Hudi
Lesson 2: Managing Data Upserts and Deletes with Apache Hudi
Lesson 3: Streaming Data Ingestion into Hadoop via Apache Hudi
Lesson 4: Performance Tuning and Best Practices for Hudi on Hadoop
Lesson 5: Troubleshooting and Debugging Hudi Operations in Hadoop
Chapter 16: Recent Updates in Apache Hadoop
Lesson 1: Major Features in the Latest Releases of Hadoop
Lesson 2: New Developments in HDFS, MapReduce, and YARN
Lesson 3: Compatibility with Modern Cloud and AI Technologies
Lesson 4: Upgrading and Migrating Hadoop Clusters
Lesson 5: Exploring Hadoop's Role in Emerging Trends (Edge Computing, IoT)
Chapter 17: Real-World Applications of Hadoop
Lesson 1: Hadoop for Large-Scale Data Analytics
Lesson 2: Using Hadoop in E-Commerce and Retail Industries
Lesson 3: Hadoop Applications in Healthcare and Genomics
Lesson 4: Financial Services Leveraging Hadoop
Lesson 5: Best Practices for Using Hadoop in Production
