Hadoop Training | Hadoop Online Course

Big Data

Hadoop

Master the power of big data with AccentFuture's comprehensive Hadoop training and online courses, crafted to provide…

accentfuture
25
45h
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Master the power of big data with AccentFuture’s comprehensive Hadoop training and online courses, crafted to provide in-depth knowledge and practical skills in data storage, processing, and analysis. Our expert-led programs are tailored for beginners and professionals alike, offering a flexible and engaging learning experience to help you harness Hadoop’s capabilities in real-world scenarios.

With AccentFuture, you’ll gain hands-on expertise in Hadoop ecosystems, including HDFS, MapReduce, Hive, and more, ensuring you’re job-ready for the evolving data landscape. Explore the best online Hadoop training with step-by-step guidance, interactive sessions, and industry-relevant projects, accessible anytime, anywhere!

What Will You Learn?

Handling Large Data Volumes: Find out more on the do’s and don’ts of big data as well as any other information concerning the big data handling.
Batch Processing: Obtain the understanding of this technology and how it functions well in handling large data in issues that require massive lots.
Real-Time Processing: Cultivate knowledge on how exactly to handle massive data in real time with the Hadoop.
Big Data Software: Education on the applications that are in use today, and which are commonly present in the current Big Data market around the world.
Data Analysis Techniques: Come to know many ways in which one can perform assessment of very large data sets.
Introduction to Big Data: Acquire fundamental understanding of Big Data and all the related elements.

Course Curriculum

Module 1: Introduction to Hadoop

1.1 Overview of Big Data and Hadoop
a) What is Big Data? (Characteristics: Volume, Velocity, Variety, Veracity, Value)
b) Introduction to Hadoop
:: History and Evolution
:: Hadoop’s role in Big Data
:: Hadoop vs. Traditional Data Processing
1.2 Hadoop Ecosystem Components
a) Core Components
:: Hadoop Distributed File System (HDFS)
:: Yet Another Resource Negotiator (YARN)
:: MapReduce
b) Ecosystem Tools
:: Apache Hive, Apache Pig, Apache HBase, Apache Spark, Apache Flink
c) Use Cases and Applications
:: Data warehousing, ETL, log analysis, real-time analytics
1.3 Setting Up Hadoop
a) Installation and Configuration
:: Installing Hadoop on local and cluster environments (single-node and multi-node setups)
:: Configuring Hadoop services: core-site.xml, hdfs-site.xml, mapred-site.xml, yarn-site.xml
b) Hadoop Command-Line Interface
:: Basic Hadoop commands for file operations (hdfs dfs)
:: Exploring the Hadoop Web UI
1.4 Hands-On Exercise: Basic Hadoop Setup
a) Installation
:: Set up a single-node Hadoop cluster
b) Configuration
:: Configure Hadoop services and navigate the Web UI

Module 2: Hadoop Distributed File System (HDFS)

2.1 HDFS Architecture
a) Components
:: Namenode, Datanode, Secondary Namenode
:: Block storage and replication
b) Data Management
:: Metadata management
:: Data distribution and fault tolerance
2.2 Working with HDFS
a) File Operations
:: Uploading and downloading files
:: Viewing and modifying file permissions
b) Directory Management
:: Creating, deleting, and listing directories
c) File Permissions
:: Setting file permissions and ownership
2.3 HDFS Optimization
a) Performance Tuning
:: Configuring block size and replication factors
:: Managing data locality and balancing
b) Monitoring and Troubleshooting
:: Using Hadoop logs and monitoring tools
:: Identifying and resolving common HDFS issues
2.4 Hands-On Exercise: Managing Data in HDFS
a) File Operations
:: Perform file operations and manage permissions
b) Optimization
:: Optimize HDFS configuration and performance

Module 3: MapReduce Programming

3.1 Introduction to MapReduce
a) Concepts
:: Map and Reduce phases
:: Data processing flow
b) Components
:: Mapper, Reducer, Combiner, Partitioner
3.2 Developing MapReduce Jobs
a) Programming Model
:: Writing MapReduce applications in Java
:: Understanding the MapReduce API
b) Job Configuration
:: Setting up job parameters and configurations
c) Debugging
:: Handling job failures and debugging
3.3 Advanced MapReduce Topics
a) Optimization Techniques
:: Performance tuning for MapReduce jobs
:: Data compression and serialization
b) Job Management
:: Monitoring and managing job execution
3.4 Hands-On Exercise: Building and Optimizing MapReduce Jobs
a) Job Development
:: Develop a MapReduce job for a real-world task (e.g., word count, log analysis)
b) Optimization
:: Optimize and troubleshoot MapReduce jobs

Module 4: Hadoop Ecosystem Tools

4.1 Introduction to Apache Hive
a) Overview
:: Hive architecture: Metastore, Driver, Execution Engine
:: HiveQL (Hive Query Language) basics
b) Data Warehousing
:: Schema design and data modeling
:: Integration with HDFS
4.2 Introduction to Apache Pig
a) Overview
:: Pig architecture and components
:: Pig Latin scripting language
b) Data Processing
:: Loading, transforming, and storing data
:: Advanced Pig features: User Defined Functions (UDFs)
4.3 Introduction to Apache HBase
a) Overview
:: HBase architecture: RegionServer, HMaster
:: Data model: Tables, rows, columns
b) Operations
:: CRUD operations and schema design
:: Real-time data access
4.4 Hands-On Exercise: Using Hive, Pig, and HBase
a) Hive
:: Write and execute Hive queries
b) Pig
:: Develop Pig scripts for data transformation
c) HBase
:: Perform HBase operations and queries

Module 5: Advanced Hadoop Components

5.1 Introduction to YARN (Yet Another Resource Negotiator)
a) Architecture
:: YARN components: ResourceManager, NodeManager, ApplicationMaster
b) Resource Management
:: Configuring resource allocation and job scheduling
5.2 Introduction to Hadoop Streaming
a) Overview
:: Using Hadoop Streaming for non-Java applications
:: Writing streaming jobs in Python, Ruby, etc.
b) Development
:: Creating and running streaming applications
5.3 Introduction to Apache Flink
a) Overview
:: Apache Flink for stream and batch processing
:: Comparison with Hadoop MapReduce and Spark
b) Integration
:: Using Flink with Hadoop
5.4 Hands-On Exercise: Using YARN, Hadoop Streaming, and Flink
a) YARN
:: Deploy and manage applications
b) Hadoop Streaming
:: Develop and execute a streaming job
c) Flink
:: Implement real-time data processing with Flink

Module 6: Performance Tuning and Best Practices

6.1 Performance Tuning Basics
a) Metrics and Monitoring
:: Key performance metrics for Hadoop
:: Monitoring tools and logs
b) Optimization Techniques
:: Tuning HDFS and MapReduce for better performance
:: Managing data locality and resource usage
6.2 Data Management Best Practices
a) Efficient Data Handling
:: Data storage and retrieval best practices
b) Security and Compliance
:: Implementing data security and compliance measures
6.3 Monitoring and Troubleshooting
a) Tools
:: Using monitoring tools for performance and health checks
b) Troubleshooting
:: Identifying and resolving common issues in Hadoop clusters
6.4 Hands-On Exercise: Performance Tuning and Best Practices
a) Optimization
:: Tune and optimize a Hadoop cluster and MapReduce jobs
b) Data Management
:: Apply best practices for data management and security

Module 7: Real-World Projects and Case Studies

7.1 Industry Case Studies
a) Applications
:: Case studies from different industries (e.g., finance, healthcare, retail)
b) Insights and Best Practices
:: Lessons learned and best practices from real-world implementations
7.2 Project
a) Project Development
:: Develop a comprehensive Hadoop-based solution involving HDFS, MapReduce, and ecosystem tools
b) Presentation and Review
:: Present the project, receive feedback, and iterate
7.3 Future Trends and Continued Learning
a) Emerging Technologies
:: Trends in big data and Hadoop advancements
b) Resources
:: Additional resources for advanced learning and certifications

Course Resources

a) Documentation
:: Official Hadoop and ecosystem tools documentation
b) Code Examples
:: GitHub repository with sample code and project templates
c) Tools and Libraries
:: Links to essential libraries and tools
d) Community
:: Access to forums, discussion groups, and Hadoop community resources

Assignments and Evaluation

a) Weekly Assignments
:: Practical exercises and tasks for each module
b) Hands-On Exercises
:: Real-world scenarios and project work
c) Mid-Course Project
:: Develop a small-scale Hadoop application or solution
d) Final Capstone Project
:: Create a comprehensive Hadoop-based solution with data processing, analysis, and optimization
e) Quizzes and Assessments
:: Regular quizzes and assessments to evaluate understanding

Hadoop Live Session

By accentfuture

4 months ago

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Hadoop

What Will You Learn?

Course Curriculum

Module 1: Introduction to Hadoop

1.1 Overview of Big Data and Hadoop

a) What is Big Data? (Characteristics: Volume, Velocity, Variety, Veracity, Value)

b) Introduction to Hadoop

:: History and Evolution

:: Hadoop’s role in Big Data

:: Hadoop vs. Traditional Data Processing

1.2 Hadoop Ecosystem Components

a) Core Components

:: Hadoop Distributed File System (HDFS)

:: Yet Another Resource Negotiator (YARN)

:: MapReduce

b) Ecosystem Tools

:: Apache Hive, Apache Pig, Apache HBase, Apache Spark, Apache Flink

c) Use Cases and Applications

:: Data warehousing, ETL, log analysis, real-time analytics

1.3 Setting Up Hadoop

a) Installation and Configuration

:: Installing Hadoop on local and cluster environments (single-node and multi-node setups)

:: Configuring Hadoop services: core-site.xml, hdfs-site.xml, mapred-site.xml, yarn-site.xml

b) Hadoop Command-Line Interface

:: Basic Hadoop commands for file operations (hdfs dfs)

:: Exploring the Hadoop Web UI

1.4 Hands-On Exercise: Basic Hadoop Setup

a) Installation

:: Set up a single-node Hadoop cluster

b) Configuration

:: Configure Hadoop services and navigate the Web UI

Module 2: Hadoop Distributed File System (HDFS)

2.1 HDFS Architecture

a) Components

:: Namenode, Datanode, Secondary Namenode

:: Block storage and replication

b) Data Management

:: Metadata management

:: Data distribution and fault tolerance

2.2 Working with HDFS

a) File Operations

:: Uploading and downloading files

:: Viewing and modifying file permissions

b) Directory Management

:: Creating, deleting, and listing directories

c) File Permissions

:: Setting file permissions and ownership

2.3 HDFS Optimization

a) Performance Tuning

:: Configuring block size and replication factors

:: Managing data locality and balancing

b) Monitoring and Troubleshooting

:: Using Hadoop logs and monitoring tools

:: Identifying and resolving common HDFS issues

2.4 Hands-On Exercise: Managing Data in HDFS

a) File Operations

:: Perform file operations and manage permissions

b) Optimization

:: Optimize HDFS configuration and performance

Module 3: MapReduce Programming

3.1 Introduction to MapReduce

a) Concepts

:: Map and Reduce phases

:: Data processing flow

b) Components

:: Mapper, Reducer, Combiner, Partitioner

3.2 Developing MapReduce Jobs

a) Programming Model

:: Writing MapReduce applications in Java

:: Understanding the MapReduce API

b) Job Configuration

:: Setting up job parameters and configurations

c) Debugging

:: Handling job failures and debugging

3.3 Advanced MapReduce Topics

a) Optimization Techniques

:: Performance tuning for MapReduce jobs

:: Data compression and serialization

b) Job Management

:: Monitoring and managing job execution