Ecosystem & Components of Hadoop

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Hadoop is an open-source framework for big data processing. It’s massive and has numerous components. Each of these components handles a specific set of big data tasks. Hadoop’s extensive suite of solutions has made it an industry standard. And if you want to be a big data expert, you must become acquainted with all of its components. You can check out our online Hadoop training to learn more.

Table of Contents

Introduction to the Hadoop Ecosystem.

The Hadoop Ecosystem is a collection of open-source software tools and frameworks that collaborate to enable large-scale dataset storage, processing, and analysis. It provides a comprehensive and scalable solution for addressing big data concerns. The ecosystem is made up of many components that deal with different areas of data management and analytics.

What are the Hadoop Core Components?

Hadoop’s basic components dictate its performance, and you must understand them before leveraging other parts of the ecosystem. Hadoop’s ecosystem is large and loaded with numerous technologies. The main components are also known as modules. There are primarily the following.

Hadoop’s basic components:

HDFS

The full name of HDFS is Hadoop Distributed File System. It is the most critical Hadoop component in terms of data storage. HDFS allows you to store data across a network of distributed storage devices. It offers a range of tools that allow you to read and examine the saved data. HDFS allows you to acquire data regardless of your computer’s operating system. Read more about HDFS and its architecture.

You can operate more productively because you don’t have to adjust your system every time you encounter a new operating system. HDFS is comprised of the following components:

NameNode
DataNode
Secondary NameNode

In HDFS, the name node is also referred to as the ‘Master’. It stores the slave nodes’ metadata to track data storage. It tells you what is kept where. The master node also monitors the slave nodes’ health. Additionally, it can assign tasks to data nodes. Data is stored in nodes. In HDFS, data nodes are referred to as ‘Slave’.

Slave nodes reply to the master node’s request for health status and update it on their situation. If a slave node fails to answer to the master node’s health status request, the master node will report it as dead and allocate the work to another data node. Aside from the name and slave nodes, there is a third type of node known as the Secondary Name Node. It functions as a buffer for the master node. When the master node is turned off, it updates the data in the FinalFS image.

MapReduce

MapReduce is Hadoop’s second core component, and it can execute two tasks: map and reduce. Mapreduce is one of the best Hadoop tools for making your big data journey easier. Mapping is the process of reading data from a database and transforming it into a more accessible and useful format. Mapping allows the system to use the data for analysis by altering its format. Then follows Reduction, a mathematical function. It lowers the mapped data to a collection of defined data, allowing for more effective analysis.

It parses key-value pairs and converts them to tuples for functionality. MapReduce is useful for a variety of Hadoop operations, including data sorting and filtering. Its two components collaborate to assist with data preparation. MapReduce also monitors and schedules jobs. It is the Hadoop ecosystem’s computing node. MapReduce is primarily responsible for breaking down large data workloads into smaller ones. You can run MapReduce jobs efficiently since it supports a number of programming languages. It supports Python, C++, and even Java for application development. It’s quick and scalable, which makes it an essential part of the Hadoop ecosystem.

Working of MapReduce

Hadoop’s MapReduce programming style and processing architecture allows for the distributed processing of huge datasets. It contains two major phases: the Map phase, which divides data into key-value pairs, and the Reduce phase, which aggregates the results. MapReduce efficiently handles parallel processing and fault tolerance, making it ideal for huge data research.

YARN

YARN stands for “Yet Another Resource Negotiator.” It manages Hadoop’s resources. Resource management is also an important duty. That is why YARN is one of the most important Hadoop components. It monitors and manages the Hadoop workloads. YARN is extremely scalable and agile. It provides improved solutions for cluster utilization, which is another key benefit. Learn more about Hadoop’s YARN architecture.

YARN consists of several components, the most essential of which is the Resource Manager. The resource manager provides flexible and generic frameworks for managing resources in a Hadoop cluster. The resource manager is sometimes known as the Master.

The node manager is another critical component of YARN. It checks the app manager and container statuses in YARN. All data processing occurs within the container, and the app manager supervises the process. If the container requires additional resources to complete its data processing responsibilities, the app manager requests them from the resource manager.

Hadoop Common

Apache has provided several libraries and utilities to the Hadoop ecosystem, which can be used with its many modules. Hadoop Common allows a computer to join the Hadoop network without encountering issues with operating system compatibility or hardware. This component employs Java technologies to allow the platform to store its data within the specified system. Hadoop Common gets its name since it supplies the system with standard capabilities.