دوره جامع Apache Hadoop

آپاچی هدوپ (Apache Hadoop) یک فریمورک متن‌باز است که برای ذخیره‌سازی و پردازش توزیع‌شده مجموعه داده‌های بزرگ در خوشه‌های کامپیوتری با استفاده از یک مدل برنامه‌نویسی ساده، طراحی شده است. این فریمورک، مقیاس‌پذیری، تحمل خطا و انعطاف‌پذیری را فراهم می‌کند و آن را به سنگ بنای صنعت کلان‌داده تبدیل می‌کند. یادگیری هدوپ برای متخصصان مشتاق کلان‌داده بسیار مهم است زیرا به‌طور گسترده توسط شرکت‌ها برای مدیریت و تجزیه و تحلیل حجم عظیمی از داده‌ها مورد استفاده قرار می‌گیرد و تخصص هدوپ را به یک مهارت بسیار مورد توجه در بازار کار تبدیل می‌کند.
یک مدرس خصوصی می‌تواند با تجزیه مفاهیم پیچیده، ارائه آموزش عملی با سناریوهای دنیای واقعی و راهنمایی زبان‌آموزان از طریق بهترین شیوه‌ها و مشکلات رایج، فرآیند یادگیری را تسریع کند و اطمینان حاصل کند که آنها هم دانش نظری و هم تجربه عملی را برای دنبال کردن مطمئن فرصت‌های شغلی کلان‌داده به دست می‌آورند.

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

مدت دوره: 50 + 10 ساعت

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