Please share hadoop eco system diagram

1. HDFS (Hadoop Distributed File System)

HDFS is a distributed file system that can be installed on commodity servers, making it cost-effective.
It is designed to store large data files across multiple machines, ensuring data reliability and availability.
Key Feature: Fault tolerance through data replication, meaning data is duplicated across multiple nodes to prevent loss if any node fails.

2. YARN (Yet Another Resource Negotiator)

YARN, also known as MapReduce V2, is a framework responsible for resource management and job scheduling in a Hadoop cluster.
It manages computational resources within the cluster and allocates resources for various applications, supporting multiple data processing engines (e.g., MapReduce, Spark).
Key Feature: It enables efficient resource allocation, which improves performance and scalability of data processing tasks.

3. Flume

Flume is a service designed for collecting, aggregating, and transporting large volumes of log data into Hadoop.
It’s primarily used for gathering log data from various sources and transferring it into HDFS or other storage systems.
Key Feature: Reliable data flow, with fault-tolerant mechanisms to ensure log data reaches its destination.

4. ZooKeeper

ZooKeeper is a coordination service that helps manage distributed applications by enabling reliable coordination across cluster nodes.
It provides essential services like configuration management, synchronization, and naming, ensuring that applications work in unison.
Key Feature: High reliability and consistency, essential for maintaining the state of distributed systems.

5. Sqoop (SQL-to-Hadoop)

Sqoop is a tool for efficiently transferring data between Hadoop and relational databases or other data stores (e.g., Hive, HBase).
It’s widely used for importing data from relational databases into HDFS and exporting data from HDFS back to databases.
Key Feature: Simplifies data interchange between structured data sources and Hadoop, automating much of the data transfer process.

6. Oozie

Oozie is a workflow scheduling system for managing and coordinating Hadoop jobs.
It supports complex workflows that involve multiple Hadoop jobs, including non-MapReduce tasks.
Key Feature: Orchestration and scheduling of tasks, allowing for complex, interdependent data processing workflows.

7. Pig

Pig is a high-level scripting framework initially developed at Yahoo! that provides a language called Pig Latin.
It allows users to write complex data transformations and execute them on a Hadoop cluster, simplifying MapReduce operations.
Key Feature: Easy-to-use scripting for data processing, abstracting away much of the complexity of traditional MapReduce programming.

8. Mahout

Mahout is a scalable library for machine learning and data mining.
It includes algorithms for clustering, classification, and collaborative filtering, which are essential for processing large datasets.
Key Feature: Designed to scale with Hadoop, enabling users to implement machine learning at a large scale.

9. R Connectors

R Connectors are used within Hadoop to perform statistical analysis on cluster data.
They integrate the statistical programming language R, allowing users to apply statistical models to data stored in Hadoop.
Key Feature: Statistical analysis and visualization, leveraging R’s capabilities for data science tasks within Hadoop.

10. Hive

Hive is an open-source data warehousing solution that enables querying and analyzing large datasets stored in Hadoop.
It uses a SQL-like language called HiveQL, allowing users to write queries similar to SQL for data analysis.
Key Feature: Provides a familiar SQL interface for Hadoop, making it easier for analysts to query big data.

11. HBase

HBase is a column-oriented, non-relational (NoSQL) database that runs on top of HDFS.
It supports real-time read and write access to large datasets, making it suitable for applications that need quick access to large amounts of data.
Key Feature: Real-time data access on Hadoop, making it a preferred choice for interactive applications.

12. Ambari

Ambari is a tool for provisioning, managing, and monitoring Hadoop clusters.
It offers a graphical user interface (GUI) that simplifies managing Hadoop services, tracking system health, and performing administrative tasks.
Key Feature: Cluster management and monitoring, providing administrators with a user-friendly way to oversee Hadoop operations.

Doubtly · 1 Nov 2024 Report

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