What you will learn
- Understand the principles and challenges of Big Data
- Navigate the architecture and components of the Hadoop ecosystem
- Develop and run MapReduce jobs for distributed data processing
- Work with Hadoop ecosystem components for data integration and storage
- Analyze and query large datasets using Hadoop tools
- IImplement security measures and best practices in Hadoop clusters
- Explore advanced topics such as performance tuning and fault tolerance
Beneficial for
- Data Engineers
- Big Data Analysts
- Database Administrators
- Software Developers interested in Big Data
Course Pre-requisite
- Basic understanding of data processing concepts
- Familiarity with programming (preferably Java or Python)
- Enthusiasm for working with large datasets and distributed computing is key.
Course Outline
Understanding the fundamentals of Big Data
Characteristics and challenges of handling large datasets
Overview of Big Data technologies and use cases
Overview of the Hadoop ecosystem
Hadoop Distributed File System (HDFS) architecture
Role of NameNode, DataNode, ResourceManager, and NodeManager
Understanding the MapReduce programming model
Writing and executing MapReduce jobs in Hadoop
Advanced MapReduce concepts and optimization techniques
Introduction to Hadoop YARN (Yet Another Resource Negotiator)
Managing and scheduling resources in Hadoop clusters
Running distributed applications on YARN
Overview of key Hadoop ecosystem components (Hive, Pig, HBase, Sqoop, etc.)
Use cases and scenarios for each ecosystem component
Integrating different components for end-to-end data processing
Importing and exporting data with Sqoop
Data transformation and processing with Apache Pig
Real-time data processing with Apache Kafka and Storm
Storing and managing structured data with Apache Hive
Schema design and optimization in Hive
NoSQL data storage with Apache HBase
Querying large datasets with Apache HiveQL
Running complex analytical queries with Apache Pig
Introduction to Apache Spark for in-memory data processing
Implementing security measures in Hadoop clusters
Authentication and authorization in Hadoop
Securing data at rest and in transit in Hadoop
Performance tuning and optimization in Hadoop
High availability and fault tolerance in Hadoop clusters
Emerging trends and future considerations in the Big Data landscape
