Alright, let's craft 100 chapter titles for a comprehensive Hadoop learning journey, from absolute beginner to expert:
Beginner (Foundation & Basics):
- Welcome to Hadoop: Your Journey into Big Data Begins
- Understanding Big Data: The What, Why, and How
- Hadoop Ecosystem: An Overview of Core Components
- Hadoop Architecture: HDFS and MapReduce Explained
- Setting Up Your Hadoop Environment: Installation Guide
- Understanding HDFS: The Hadoop Distributed File System
- HDFS Architecture: NameNode, DataNodes, and Blocks
- Working with HDFS: Basic Commands and Operations
- Understanding MapReduce: The Processing Engine
- MapReduce Workflow: Map, Shuffle, and Reduce Phases
- Writing Your First MapReduce Program: Word Count Example
- Compiling and Running MapReduce Jobs
- Understanding Input and Output Formats in MapReduce
- Introduction to YARN: Resource Management in Hadoop
- YARN Architecture: ResourceManager, NodeManager, and ApplicationMaster
- Understanding Hadoop Configuration Files: Core-site.xml, Hdfs-site.xml, etc.
- Basic Hadoop Administration: Starting and Stopping Services
- Viewing Hadoop Logs: Troubleshooting and Debugging
- Introduction to Hadoop Streaming: Using Non-Java Languages
- Introduction to Hadoop Archives (HAR): Efficient Data Storage
- Understanding Sequence Files: Binary File Formats
- Understanding Avro Files: Schema-Based Data Serialization
- Introduction to Parquet Files: Columnar Storage
- Introduction to ORC Files: Optimized Row Columnar Format
- Basic Data Ingestion into HDFS: Using
put and copyFromLocal
Intermediate (Advanced MapReduce & Ecosystem Tools):
- Advanced MapReduce Patterns: Joins, Filtering, and Aggregation
- Custom Partitioners: Controlling Data Distribution
- Custom Comparators: Sorting and Grouping Data
- Combiners: Optimizing MapReduce Performance
- Distributed Cache: Sharing Data Across MapReduce Tasks
- Understanding Counters: Monitoring Job Progress
- Writing Custom Writable Classes: Serializing Complex Data
- Introduction to Hadoop Distributed File System Shell (HDFS Shell)
- Understanding HDFS Permissions and Security
- Introduction to Apache Pig: Data Flow Language
- Writing Pig Scripts: Data Transformation and Analysis
- Understanding Pig UDFs: Custom Functions in Pig
- Introduction to Apache Hive: Data Warehousing on Hadoop
- Writing HiveQL Queries: SQL-like Interface for Hadoop
- Hive Data Types and Schemas: Defining Your Data
- Hive Partitioning and Bucketing: Optimizing Queries
- Hive UDFs and UDAFs: Extending Hive Functionality
- Introduction to Apache HBase: NoSQL Database on Hadoop
- HBase Architecture: RegionServers, HMaster, and Zookeeper
- Working with HBase Shell: Basic Operations
- HBase Data Modeling: Designing Your Tables
- Introduction to Apache Sqoop: Data Transfer Between Hadoop and Relational Databases
- Importing Data from MySQL to HDFS with Sqoop
- Exporting Data from HDFS to MySQL with Sqoop
- Introduction to Apache Flume: Log Data Collection
- Flume Agents: Sources, Channels, and Sinks
- Understanding Flume Configurations: Designing Data Flows
- Introduction to Apache Oozie: Workflow Scheduling
- Defining Oozie Workflows: Using XML
- Scheduling Oozie Jobs: Coordinating Hadoop Tasks
- Introduction to Apache ZooKeeper: Distributed Coordination Service
- ZooKeeper Architecture: Nodes and Sessions
- Using ZooKeeper for Distributed Coordination
- Hadoop Security: Kerberos Authentication
- Hadoop Performance Tuning: Optimizing Job Execution
- Understanding Hadoop Cluster Monitoring: Tools and Techniques
- Troubleshooting Common Hadoop Issues
- Understanding Hadoop Federation: Scaling HDFS
- Hadoop High Availability: Ensuring Cluster Resilience
- Working with Hadoop Distributions: Cloudera, Hortonworks, etc.
Advanced (Spark Integration, Real-Time Processing & Deployment):
- Introduction to Apache Spark: In-Memory Data Processing
- Spark Architecture: RDDs, DataFrames, and Datasets
- Integrating Spark with Hadoop: Reading and Writing Data
- Using Spark SQL for Data Analysis on Hadoop
- Spark Streaming: Real-Time Data Processing on Hadoop
- Spark Machine Learning Library (MLlib): Building Predictive Models
- Spark GraphX: Graph Processing on Hadoop
- Introduction to Apache Kafka: Distributed Streaming Platform
- Integrating Kafka with Hadoop: Real-Time Data Ingestion
- Lambda Architecture: Combining Batch and Stream Processing
- Kappa Architecture: Stream-Based Data Processing
- Building Real-Time Data Pipelines on Hadoop
- Advanced Hadoop Security: Ranger and Sentry
- Hadoop Cluster Capacity Planning: Sizing Your Infrastructure
- Hadoop Cluster Deployment: Best Practices and Automation
- Containerizing Hadoop with Docker and Kubernetes
- Hadoop on Cloud Platforms: AWS EMR, Azure HDInsight, GCP Dataproc
- Managing Hadoop in the Cloud: Scaling and Optimization
- Hadoop Data Governance: Data Lineage and Metadata Management
- Advanced Hadoop Performance Tuning: JVM Optimization
- Hadoop Resource Management: Fair Scheduler and Capacity Scheduler
- Hadoop Data Compression: Optimizing Storage and Network Usage
- Advanced HDFS Management: Erasure Coding and Storage Policies
- Building Data Lakes with Hadoop: Best Practices
- Hadoop and Data Warehousing: Modernizing Data Infrastructure
- Hadoop and Machine Learning: Building Scalable Models
- Hadoop and IoT Data Processing: Real-Time Analytics
- Hadoop and Graph Databases: Analyzing Relationships
- Hadoop and Search: Integrating with Solr and Elasticsearch
- Hadoop and Data Science: Building Analytical Pipelines
- Hadoop and DevOps: Automating Deployments and Management
- Hadoop and Security Information and Event Management (SIEM)
- Case Studies: Real-World Hadoop Implementations
- The Future of Hadoop: Trends and Innovations
- Hadoop Certification Preparation: Tips and Strategies