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. Learn more about Hadoop and its ecosystem on the official Apache Hadoop website.

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: 

MapReduce

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. 

Pig

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). 

Hive

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). 

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Hadoop Ecosystem Architecture With Diagram

Here's a Hadoop ecosystem diagram - 

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.

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 

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