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Hadoop Components that you need to Know about

In today’s data-driven world, the volume and variety of data being generated are increasing drastically. The ability to effectively store, manage, and analyze this data has become critical for organizations of all sizes. Hadoop is one such technology that has emerged as a leading platform for big data processing. In this article, we’ll explore the various components that make up the Hadoop ecosystem and their roles in the big data processing pipeline. To know more about Hadoop components you can visit our curated course on Big Data.

Components of Hadoop

The most crucial components that are used by all professionals are here listed below:

Hadoop Distributed File System (HDFS)

HDFS is the core storage system of Hadoop. It is a distributed file system that is designed to store and manage large datasets across multiple nodes in a Hadoop cluster. HDFS provides high fault tolerance by replicating data across multiple nodes, ensuring that data is not lost in case of hardware failure.


YARN stands for Yet Another Resource Negotiator. It is the resource management layer of Hadoop that manages and allocates resources to various applications running in the cluster. YARN enables Hadoop to run a wide range of processing frameworks such as Apache Spark, Apache Flink, and Apache HBase.


MapReduce is a programming model for processing and analyzing large datasets in a distributed environment. It works by breaking down a large dataset into smaller chunks and processing them in parallel across multiple nodes in the cluster. MapReduce consists of two main phases: the map phase and the reduce phase.


Hive is a data warehouse infrastructure that is built on top of Hadoop. It provides a SQL-like interface to query data stored in Hadoop, making it easy for users familiar with SQL to work with Hadoop data. Hive also provides an optimized query execution engine that can handle complex queries on large datasets.

Apache Spark

Apache Spark is a fast and general-purpose distributed computing system that is designed for large-scale data processing. It provides an in-memory processing engine that can be used for real-time processing, machine learning, and graph processing.


HBase is a distributed, non-relational database that is built on top of Hadoop. It provides random access to large amounts of structured and semi-structured data. HBase is commonly used for real-time data processing, such as serving data for web applications.


Pig is a high-level platform for creating MapReduce programs used for analyzing large datasets. It provides a scripting language called Pig Latin that is used to create data processing workflows that can be executed on Hadoop. Pig provides a higher level of abstraction compared to MapReduce, making it easier for users to work with large datasets.


ZooKeeper is a distributed coordination service that is used to manage and coordinate distributed applications in Hadoop. It provides a set of primitives that can be used to build distributed applications such as leader election, synchronization, and configuration management.


Hadoop is a powerful platform for big data processing, and its ecosystem consists of several components that work together to enable efficient storage, processing, and analysis of large datasets. By understanding the various components of the Hadoop ecosystem, you can make better use of this platform to extract valuable insights from your data. Our Big Data course can be one of the best options to get a better understanding of the components of Hadoop. 

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