Hadoop Online Training

Four Main Modules in Hadoop Online Training:

1. Hadoop Distributed File System (HDFS):
   • HDFS is a distributed file system designed to run on low-cost hardware. It is optimized for high throughput and offers fault tolerance, making it suitable for storing and processing large datasets.
   • Key Features:
     • High fault tolerance with data replication.
     • Efficient storage for large datasets.
     • Runs on standard or low-end hardware.
2. Yet Another Resource Negotiator (YARN):
   • YARN manages and schedules resources in a Hadoop cluster, overseeing job execution and monitoring cluster nodes.
   • Key Features:
     • Resource management and allocation.
     • Job scheduling and task monitoring.
     • Efficient resource utilization across the cluster.
3. MapReduce:
   • MapReduce is the core computational model in Hadoop, where data is processed in parallel across multiple nodes.
   • Key Features:
     • Map Task: Takes input data and splits it into key-value pairs.
     • Reduce Task: Aggregates the output of map tasks and generates the final result.
     • Ideal for batch processing large datasets.
4. Hadoop Common:
   • This module provides common Java libraries that can be used by all other modules in Hadoop, helping to simplify development and ensure compatibility across the Hadoop ecosystem.

How Hadoop Works:

• Hadoop enables the storage and processing of data across a distributed environment by utilizing clusters of computers to work in parallel, improving efficiency and scalability.
• Applications can submit data in various formats to Hadoop by using the API to connect with the NameNode, which manages the file directory and the placement of data blocks across the DataNodes.
• The Hadoop ecosystem is vast and continuously growing. It includes various tools for collecting, storing, processing, analyzing, and managing big data efficiently.

The Hadoop Ecosystem:

The Hadoop ecosystem is made up of several projects and tools that complement Hadoop’s functionality, such as:

• Hive: Data warehouse for querying and managing large datasets.
• Pig: A platform for analyzing large datasets using a high-level scripting language.
• HBase: A distributed, scalable NoSQL database.
• Spark: A fast, in-memory processing engine for big data analytics.
• Sqoop: A tool for transferring data between Hadoop and relational databases.
• Flume: A service for efficiently collecting, aggregating, and moving large amounts of log data.

Prerequisites for Hadoop Training:

• No prerequisites are required, but having a basic understanding of Core Java and SQL can be beneficial for understanding the underlying concepts and frameworks in Hadoop.

Why Learn Hadoop with Trailevate Solution?

• Comprehensive Curriculum: Learn Hadoop from the ground up, covering all essential components and tools.
• Hands-on Training: Practical exercises and real-world use cases to master Hadoop’s features and functionalities.
• Expert Instructors: Get trained by industry experts who have hands-on experience with big data technologies.
• Flexible Learning: Access the course content anytime, anywhere, with online training options to fit your schedule.