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Understanding Hadoop Ecosystem: Architecture, Components & Tools

27th Dec, 2023
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    Understanding Hadoop Ecosystem: Architecture, Components & Tools

    The Hadoop ecosystem is one of the most critical developments in Big Data. It provides tools for storing and analyzing data and a framework for other companies to develop their applications.

    This means that you can use the Hadoop ecosystem to build your applications, or you can use it just as an infrastructure tool for storing and analyzing your data. This flexibility makes it a potent option for enterprise-level companies with large amounts of data that need to be analyzed efficiently.

    Continue reading to get a brief overview of the Hadoop ecosystem.

    What Is Hadoop?

    Hadoop is a software framework that enables you to store and process large amounts of data on a cluster of computers. 

    Hadoop comprises two distinct parts - 

    1. HDFS (Hadoop Distributed File System)

    It enables you to store large amounts of data across multiple servers in a distributed manner, so it's easier for you to access it via requests from your clients or applications. 

    2. MapReduce

    It enables your computer to process these requests quickly, no matter how large. 

    Continue reading to get a quick introduction to the Hadoop ecosystem.

    What Is the Hadoop Ecosystem?

    The Hadoop Ecosystem is a collection of tools, libraries, and frameworks that help you build applications on top of Apache Hadoop.

    Hadoop provides massive parallelism with low latency and high throughput, which makes it well-suited for big data problems. 

    The Hadoop ecosystem definition extends this functionality with additional tools to make it easier to use Hadoop with other frameworks like Spark or Kafka for real-time processing or machine learning tasks. We've explained the Hadoop ecosystem in detail throughout the article.

    Introduction To Hadoop Ecosystem For Big Data

    Hadoop consists of several tools that work together to process and analyze vast amounts of data. It is an open-source framework, and its architecture is based on widely distributed systems. These tools include: 


    This tool divides the input into small pieces, distributes them across many machines in the cluster, and combines the output from all machines into one file. 


    This tool allows you to write scripts in a language called Pig Latin that can be used to query large datasets stored in Hadoop Distributed File System (HDFS). 


    This tool allows users to store data in tables similar to those already present in SQL databases. Still, it is stored as files on HDFS instead of being stored in relational database management systems (RDBMS). 

    To become a programming professional, look at the Big Data and Hadoop course to get the best tips and tricks. 

    Hadoop Ecosystem Architecture With Diagram

    Here's a Hadoop ecosystem diagram - 

    Hadoop ecosystem

    The Hadoop ecosystem architecture is made up of four main components: data storage, data processing, data access, and data management. 

    1. Data Storage 

    The first step to explaining the Hadoop ecosystem is where all your raw data is stored. It could be on a local hard drive or in the cloud. 

    2. Data Processing

    The second phase of the Hadoop ecosystem in Big Data involves analyzing your data and transforming it into something meaningful that can be used for further analysis.

    3. Data Access 

    In this third phase of the Hadoop ecosystem, you can use tools like Hive or Pig to query your data sets and perform actions like filtering out specific rows, sorting them by certain columns or values within them (such as location or birthdate), etc. 

    4. Data Management 

    Finally, the last phase of the Hadoop ecosystem architecture involves taking all the work we've done on data sets in previous phases and storing it safely somewhere so we can return to it later if needed.

    Enroll in the Big Data training to understand the data flow process. 

    Hadoop Ecosystem Fundamental Tools

    Hadoop and its ecosystem include many tools for data processing and analysis. Some of these tools are used to collect data from various sources, while others are used to store and analyze the data. 

    Here's a list of fundamental Hadoop ecosystem tools that you should know about:

    1. Oozie - Workflow Monitoring

    Oozie is a workflow management system that allows users to monitor and control workflows. It can be used to automate tasks for a variety of purposes, including data processing, system administration, and debugging. 

    2. Chukwa – Monitoring

    Chukwa is an open-source distributed monitoring system for high-performance computing clusters. The tool collects data from Hadoop Distributed File System (HDFS), MapReduce, and YARN applications. It provides a web interface to view the data collected by Chukwa agents running on each node in the cluster. 

    3. Flume – Monitoring

    Flume is an open-source distributed log collection system storing log events from sources such as web servers or application servers into HDFS or other systems.

    4. Zookeeper – Management

    It is a management tool that helps with the configuration management, data synchronization, and service discovery functions of Hadoop clusters. 

    5. Hive – SQL

    Hive is a data warehouse system for Hadoop that allows users to query data using Structured Query Language (SQL). It can also be used to create and modify tables and views, grant privileges to users, and so on. 

    6. Pig – Dataflow

    Pig is a high-level language for writing data transformation programs. It provides a way to express data analysis programs, like how people speak about their work. Pig programs are compiled into MapReduce jobs that run on the Hadoop infrastructure. 

    7. Mahout - Machine Learning

    Mahout is a suite of machine-learning libraries that run on top of Hadoop. It includes implementing many standard algorithms such as k-means clustering, naïve Bayes classification, logistic regression, support vector machines (SVM), random forests, etc. 

    8. MapReduce - Cluster Management

    It is a programming model frequently used for processing and managing large datasets. It has two phases:

    • Map Phase: we divide the input data into chunks and process it in parallel. 
    • Reduce phase: each group of intermediate key-value pairs is passed to a reducer which computes the final output based on the values in that group. 

    9. HBASE - Column DB Storage

    HBase (Hadoop Base) is an open-source database that uses HDFS as its underlying storage system. It provides a NoSQL storage solution for storing large amounts of unstructured data in a scalable manner. 

    Hadoop Ecosystem Frameworks

    The Hadoop ecosystem is an ever-growing collection of tools, libraries, and add-ons that can be used to build applications on top of the Apache Hadoop platform. The following are some of the most popular frameworks in this ecosystem.

    Apache Avro 

    Avro provides a compact serialization format that allows you to write your data once and read it anywhere. This means you don't have to worry about translating your data into different formats when moving it between other systems. 


    Thrift is an RPC framework for writing services in C++ or Java that communicate across languages and platforms. It enables you to write code once and then run it on any platform without having to rewrite it. 

    Features Of the Hadoop Ecosystem

    The Hadoop ecosystem includes the following four features that explain the Hadoop ecosystem in detail - 

    1. It's Free, Open-source Software

    Hadoop is free, open-source software that allows you to process large datasets in a distributed manner. The software is built with the help of Java and can be used on commodity hardware. 

    2. It's Highly Scalable And Fault-tolerant

    Hadoop is designed for large datasets and can be scaled up to multiple terabytes of data by adding more nodes. Also, it is fault-tolerant. 

    3. It runs on commodity hardware

    All the Hadoop ecosystem components are standard components like Linux servers, hard drives, and network switches which you can easily buy from any vendor at affordable prices. 

    4. It Is Designed For Huge Datasets

    The most common use case for Hadoop is storing large amounts of unstructured data—which traditional SQL databases struggle with because they're optimized for structured data. 

    Components of the Hadoop Ecosystem - A Complete Tutorial

    Hadoop ecosystem

    The big data and Hadoop ecosystem is a vast network of components working together to provide a unified experience for data processing. 

    These Hadoop ecosystem components include - 

    1. HDFS: Hadoop Distributed File System

    HDFS is a distributed file system that runs on top of the Hadoop cluster. It is responsible for storing the data and managing access to it. The data stored on HDFS is split into blocks, which are then replicated across multiple nodes in the cluster. 

    2. YARN: Yet Another Resource Negotiator

    YARN is a programming framework and a component of the Hadoop ecosystem that specifies how jobs should be run and managed on Hadoop clusters. It allows users to submit applications on different machines within the cluster, with each job running on a single machine called a container. 

    3. MapReduce: Programming-based Data Processing

    It is a programming model for processing large data sets. It divides the data into chunks and distributes them across multiple cluster nodes.

    4. Spark: In-Memory Data Processing

    Spark is an in-memory data processing framework and one of the main components of the Hadoop ecosystem that can be used to run MapReduce jobs. It was designed to overcome some limitations of Hadoop MapReduce, such as slow performance on large datasets and low memory utilization when running iterative algorithms like machine learning models for prediction purposes. 

    Key Benefits Of the Hadoop Ecosystem 

    Here are some of the notable benefits of the Hadoop Ecosystem! 

    1. It Helps You Store, Manage, And Process Data

    The Hadoop ecosystem is a collection of open-source software tools that allow you to store and process large amounts of data. The tools in this ecosystem include HDFS (Hadoop Distributed File System), YARN (Yet Another Resource Negotiator), and MapReduce. These tools allow you to store huge amounts of data on your servers without paying for expensive third-party solutions. 

    2. It Allows You To Run Analytics On Large Datasets

    The Hadoop ecosystem allows you to run analytics on large datasets using MapReduce programming models. This approach lets you run complex algorithms on your data without purchasing expensive hardware or software solutions from third-party vendors. 

    3. It Enables Real-time Analysis Of Streaming Data

    The Hadoop ecosystem is designed to process large amounts of data in real time. This can be useful for many applications, such as financial transactions and tracking customer behavior. 

    4. It offers Security For Your Data

    Hadoop offers some degree of security through encryption and authentication methods that protect your data from being accessed by unauthorized users or systems. 

    Uses Of Components Of the Hadoop Ecosystem 

    The Hadoop ecosystem is vast and includes many different components. These Hadoop ecosystems and their components are used in different ways and for various purposes. 

    Integrating with other systems is one of the most common uses for a Hadoop component. By connecting other systems to Hadoop, you can use your existing data sources to analyze, store, and analyze data from different angles.

    Another common use for a component of Hadoop is as part of a larger analytics platform that allows people throughout your organization to access and manipulate data stored on Hadoop clusters.

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    The Hadoop ecosystem is a complex and ever-changing space. To get the most out of your learning experience, you need a Hadoop administration course to help you keep up with the latest developments. 

    In this article, we have learned everything from the history of Hadoop, an overview of Hadoop ecosystem architecture, and tools to its current applications. You'll understand what makes Hadoop unique and how it differs from other similar frameworks. You'll also look at its key features, including YARN and Spark. 

    Whether new to Hadoop or looking for more advanced knowledge, we're here to help you succeed!

    Hadoop ecosystem FAQs

    1How many different technologies are in the Hadoop ecosystem?

    There are many different technologies in the Hadoop ecosystem in big data, but three of the most common are HDFS, MapReduce, and HBase.

    2What is the main idea of Hadoop giving its ecosystem tools?

    The main idea behind Hadoop giving its ecosystem tools is to make it easier for companies to use Hadoop. By making the tools, they can start using Hadoop immediately and don't have to spend time creating their tools or figuring out how to make them.

    3What is YARN in the Hadoop ecosystem?

    YARN, or Yet Another Resource Negotiator, is a cluster management system for Apache Hadoop. It's responsible for scheduling and executing application jobs on the cluster.

    4What is the big data ecosystem?

    The big data ecosystem is a term that describes the many different technologies, frameworks, and processes used to manage, analyze, and store large amounts of data.

    5What are the challenges with the Hadoop ecosystem?

    The biggest challenge with the Hadoop ecosystem is that it is difficult to integrate multiple data sources and use cases into one cohesive platform. This often results in organizations that want to use Hadoop being forced to choose between a limited number of tools or building their custom integration tool from scratch.


    Dr. Manish Kumar Jain

    International Corporate Trainer

    Dr. Manish Kumar Jain is an accomplished author, international corporate trainer, and technical consultant with 20+ years of industry experience. He specializes in cutting-edge technologies such as ChatGPT, OpenAI, generative AI, prompt engineering, Industry 4.0, web 3.0, blockchain, RPA, IoT, ML, data science, big data, AI, cloud computing, Hadoop, and deep learning. With expertise in fintech, IIoT, and blockchain, he possesses in-depth knowledge of diverse sectors including finance, aerospace, retail, logistics, energy, banking, telecom, healthcare, manufacturing, education, and oil and gas. Holding a PhD in deep learning and image processing, Dr. Jain's extensive certifications and professional achievements demonstrate his commitment to delivering exceptional training and consultancy services globally while staying at the forefront of technology.

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