Big Data, Hadoop and its Architecture

The objective of this program is to provide participants with a comprehensive understanding of Big Data concepts and the Hadoop ecosystem. The course focuses on storing, processing, and analyzing large-scale datasets efficiently using Hadoop and its components. Participants will learn how to design scalable data architectures and implement distributed data processing solutions for real-world business scenarios.

Category: Analytics

Description

1. Learning Outcomes

Understand the fundamentals of Big Data, its challenges, and use cases across industries.
Learn the Hadoop ecosystem including HDFS, MapReduce, YARN, Hive, Pig, and HBase.
Design and implement distributed storage and processing systems for large datasets.
Apply MapReduce programming for batch processing of Big Data.
Use Hive and Pig for querying and analyzing structured and semi-structured data.
Integrate Hadoop with other Big Data tools for analytics and reporting.
Optimize performance and ensure reliability in distributed data processing environments.

2. Program Highlights & Key Features

Hands-on exercises with HDFS, MapReduce, Hive, and Pig.
Expert-led sessions on Hadoop architecture, cluster setup, and data processing workflows.
Case studies covering finance, healthcare, e-commerce, and telecom Big Data challenges.
Training on data ingestion, storage, and retrieval in distributed systems.
Exposure to emerging trends like Spark integration, cloud-based Hadoop, and real-time Big Data analytics.

Module 1: Introduction to Big Data and Project Management
Module 2: Big Data Discovery and Project Goals
Module 3: Big Data Project Life Cycle
Module 4: Big Data Team Structure and Key Activities
Module 5: People, Deliverables, and Key Success Factors

1.1 Introduction to Big Data

Concept and definition of Big Data
Need for Big Data in modern organizations
Traditional data vs Big Data
Business problems solved using Big Data

1.2 Characteristics of Big Data

The 5 Vs of Big Data

1. Volume
2. Velocity
3. Variety
4. Veracity
5. Value

1.3 Introduction to Project Management

Concept of a project
Characteristics of a project
Difference between operations and projects

1.4 Importance of Big Data Projects

Role of Big Data in decision-making
Business value of Big Data projects
Use cases across industries

2.1 Big Data Discovery

Understanding data sources
Structured, semi-structured, and unstructured data
Data discovery techniques

2.2 Business Goals of Big Data

Increasing business value and revenue
Resource optimization and cost reduction
Data-driven decision making

2.3 Big Data Architecture

Overview of Hadoop architecture

1. HDFS
2. YARN
3. MapReduce

2.4 Big Data Environment Setup

Installation of Big Data tools
Configuring Cloudera on Windows system
Understanding the Big Data ecosystem tools

1. HDFS
2. Hive
3. Sqoop
4. MySQL

3.1 Big Data Project Life Cycle Phases

Big Data Planning
Big Data Development
Operations and Support
Evaluation and Optimization

3.2 Data Ingestion using Sqoop

Sqoop architecture
Importing data from MySQL to HDFS
Exporting data from HDFS to MySQL

3.3 Working with Hive

Introduction to Hive
Hive architecture
Internal tables vs External tables
Basic HiveQL commands.

3.4 MySQL Basics for Big Data

Basic MySQL concepts
Creating databases and tables
Integrating MySQL with Sqoop

4.1 Big Data Team Structure

Roles and responsibilities in a Big Data team

1. Data Engineer
2. Data Analyst
3. Data Scientist
4. Project Manager
5. Business Analyst

4.2 Key Activities in Big Data Projects

Requirement gathering
Data ingestion and processing
Data storage and management
Analysis and reporting

4.3 Role of Data Engineer

Data pipeline development
Data integration and transformation
Performance optimization
Ensuring data quality and reliability

4.4 Complete Big Data Project Execution

End-to-end project flow
From data source to analytics output

5.1 Big Data Tools and Their Roles

Role of Sqoop, HDFS, Hive, and MySQL
Tool selection based on project needs

5.2 Hive Data Optimization Concepts

Partitioning in Hive
Bucketing in Hive
Benefits of partitioning and bucketing

5.3 Hands-on Analysis

Country-wise data analysis using partitioning
State-wise data analysis using bucketing

5.4 Hive as a Data Warehouse

Why Hive is used as a data warehouse
Hive vs traditional data warehouses

5.5 End-to-End Big Data Project (Hands-on)

Data ingestion using Sqoop
Storage in HDFS
Data modeling and querying in Hive
Integration with MySQL
Complete Big Data data pipeline execution

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