Big Data

Hadoop

Hadoop Training: Apache Hadoop is a scalable, open-source framework designed to handle large-scale data storage and distributed…

accentfuture
25
45h
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Hadoop Training:

Apache Hadoop is a scalable, open-source framework designed to handle large-scale data storage and distributed processing efficiently. It enables organizations to store, manage, and analyze massive datasets that traditional databases cannot handle. By distributing data across multiple nodes in a cluster, Hadoop ensures fault tolerance, high availability, and parallel computing for big data applications.

Hadoop is built on a distributed computing model, allowing organizations to process large amounts of data across multiple machines simultaneously. It follows the MapReduce programming model, where large datasets are broken into smaller chunks, processed in parallel, and aggregated to deliver meaningful insights. Hadoop consists of HDFS (Hadoop Distributed File System) for storage, MapReduce for processing, and YARN for resource management. It supports various tools like Hive, Pig, HBase, Spark, and Sqoop to extend its functionality.

This Hadoop Training course will take you from Hadoop fundamentals to advanced big data processing. You’ll learn Hadoop concepts such as how HDFS stores large datasets, how MapReduce processes data in parallel, and how YARN manages cluster resources. It also covers Hive, Pig, HBase, and Spark for querying, scripting, and fast data processing.

Through hands-on projects, this Hadoop Online Course will guide you in setting up Hadoop clusters, processing large datasets, and automating ETL workflows. By the end of this course, you’ll be able to build scalable big data applications and optimize performance with confidence.

What Will You Learn?

Module 1: Introduction to Hadoop & Big Data: What is Big Data? Challenges & Opportunities; Introduction to Apache Hadoop & Its Importance; Hadoop Architecture Overview; Key Components: HDFS, MapReduce, YARN; Hadoop vs Traditional Databases
Module 2: Hadoop Distributed File System (HDFS): Understanding HDFS and Its Role in Big Data, Data Storage in Hadoop: Blocks & ReplicationHDFS Commands: Uploading, Retrieving, and Managing Files, Fault Tolerance & High Availability in HDFS, Hands-on: Setting Up & Managing HDFS
Module 3: MapReduce – Data Processing in Hadoop: What is MapReduce? Basics & Programming Model, Writing & Executing MapReduce Jobs (Java/Python), Understanding Key-Value Pairs & Data Processing Flow, Optimizing & Debugging MapReduce Jobs, Hands-on: Writing a Simple MapReduce Program
Module 4: YARN – Resource Management in Hadoop: What is YARN? Role in Hadoop Architecture, Resource Allocation & Job Scheduling in YARN, Managing Hadoop Clusters with YARN, Hands-on: Running Jobs & Monitoring YARN
Module 5: Hadoop Ecosystem Tools & Frameworks: Apache Hive – SQL-like querying for Big Data, Apache Pig – Data transformation using scripting, Apache HBase – NoSQL database for real-time access, Apache Sqoop & Flume – Importing & Exporting Data, Apache Oozie – Workflow Automation & Job Scheduling, Hands-on: Working with Hive, Pig, and HBase
Module 6: Integrating Hadoop with Apache Spark: Why Use Spark with Hadoop? Benefits & Use Cases, Running Spark on Hadoop Clusters, Writing Spark Jobs for Fast Data Processing, Hands-on: Processing Big Data with Spark & Hadoop
Module 7: Hadoop Cluster Setup & Administration: Installing & Configuring Hadoop on Local and Cloud (AWS, GCP, Azure), Setting Up Multi-Node Hadoop Clusters, Managing Hadoop Jobs & Logs, Monitoring Cluster Performance & Troubleshooting
Module 8: Hadoop Performance Optimization & Security: Tuning Hadoop for Better Performance, Data Compression & Partitioning Techniques, Securing Hadoop Clusters (Authentication & Authorization), Implementing Role-Based Access Control in Hadoop
Module 9: Real-World Hadoop Projects: Building an ETL Pipeline Using Hadoop, Processing Streaming Data with Hadoop & Spark, Analyzing Social Media Data Using Hive & Pig, Implementing a NoSQL Solution with HBase

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

7 months ago

Join Our Hadoop Live Session!
Dive into big data with a hands-on experience covering Hadoop fundamentals, live demos, and expert insights.
Date: 3rd Sep 2024
Time: 08:00 AM IST.
Reserve your spot now: [https://www.accentfuture.com/enquiry-form]

Hadoop

Hadoop Training:

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