Concept of Hadoop

Hadoop is an open-source framework designed to store and process large amounts of data across distributed clusters of computers. It was developed by Apache Software Foundation and is designed to handle Big Data’s volume, variety, and velocity. The main idea behind Hadoop is to use a distributed computing model to efficiently process large datasets that traditional systems cannot handle effectively.

Key Concepts:

1. Distributed Storage and Processing:

• Hadoop distributes data across a cluster of machines and processes it in parallel. This makes it scalable and fault-tolerant.

1. Scalability:

• Hadoop can scale horizontally, meaning you can add more machines to the cluster to handle increased data volumes.

1. Fault Tolerance:

• Hadoop is designed to handle hardware failures gracefully. Data is replicated across multiple nodes to ensure reliability.

1. Cost-Effectiveness:

• It uses commodity hardware, which makes it more affordable compared to traditional systems that require high-end servers.

Core Hadoop Components

1. Hadoop Distributed File System (HDFS):

• Function: HDFS is the storage layer of Hadoop. It stores large files by breaking them into blocks (typically 128MB or 256MB) and distributing them across multiple nodes in the cluster.
• Features: It is designed to be fault-tolerant, with data replication to ensure high availability. For instance, each block is replicated across multiple nodes (default is three).

1. MapReduce:

• Function: MapReduce is the processing layer of Hadoop. It processes large datasets in parallel by dividing the data into chunks and processing them in distributed fashion.
• Phases:
  • Map Phase: Processes input data and generates intermediate key-value pairs.
  • Reduce Phase: Aggregates and processes the intermediate data to produce the final output.

1. YARN (Yet Another Resource Negotiator):

• Function: YARN is responsible for resource management and job scheduling. It manages resources across the Hadoop cluster and schedules jobs based on available resources.
• Components:
  • ResourceManager: Manages the allocation of resources to various applications.
  • NodeManager: Manages resources on individual nodes and reports to the ResourceManager.

1. Hadoop Common:

• Function: This includes libraries and utilities required by other Hadoop modules. It provides the necessary tools and APIs to support Hadoop’s functionalities.

Hadoop Ecosystem

The Hadoop ecosystem includes various tools and projects that complement and extend Hadoop’s core functionalities. Here are some notable components:

1. Hive:

• Function: A data warehouse infrastructure built on top of Hadoop that facilitates querying and managing large datasets using a SQL-like language (HiveQL).
• Use Case: Ideal for data analysis and reporting.

1. Pig:

• Function: A high-level platform for creating MapReduce programs using a scripting language called Pig Latin.
• Use Case: Simplifies the development of complex data processing tasks.

1. HBase:

• Function: A distributed, scalable NoSQL database that runs on top of HDFS. It provides random, real-time read/write access to large datasets.
• Use Case: Suitable for real-time analytics and handling structured and semi-structured data.

1. Sqoop:

• Function: A tool for efficiently transferring data between Hadoop and relational databases (e.g., MySQL, Oracle).
• Use Case: Useful for importing and exporting data to and from Hadoop.

1. Flume:

• Function: A service for efficiently collecting, aggregating, and moving large amounts of log data from various sources to Hadoop.
• Use Case: Used for log data ingestion and management.

1. Oozie:

• Function: A workflow scheduler system to manage Hadoop jobs. It allows users to define and manage complex data processing workflows.
• Use Case: Ideal for scheduling and orchestrating batch processing jobs.

1. ZooKeeper:

• Function: A centralized service for maintaining configuration information, naming, and providing distributed synchronization.
• Use Case: Used to coordinate distributed applications.

1. Spark:

• Function: A fast, in-memory data processing engine that can work with Hadoop. It provides an alternative to MapReduce with better performance for certain workloads.
• Use Case: Suitable for real-time processing and iterative algorithms.

Summary

• Hadoop is a powerful framework for distributed storage and processing of large data sets.
• Core Components: HDFS for storage, MapReduce for processing, YARN for resource management, and Hadoop Common for essential utilities.
• Ecosystem: Includes tools like Hive, Pig, HBase, Sqoop, Flume, Oozie, ZooKeeper, and Spark, each serving different purposes to enhance Hadoop’s capabilities.