What Is Hadoop?

Big Data has risen in prominence over the last two years, and when data analysts and scientists talk about Big Data, Hadoop is one of the main terms that come to mind. But have you ever wondered what this tool is and what the fuss is all about? This article will give you the answers and explore Hadoop and its relationship with Big Data. 

Understanding Hadoop

Hadoop, which stands for High Availability Distributed Object Oriented Platform, is an open-source framework designed to address the problems of processing and storing Big Data. It uses clusters of computers, or nodes, that work together to analyze and store data from various sources. 

To illustrate what Hadoop does, imagine that you’re running a small furniture business that sells a single product, like tables. This can be accomplished by creating the tables on your own and storing the items in a single space. But what if the demand and the number of product types you sell increases?

To deliver the items on time, you need more storage space and employees to build various products in parallel and store each product type in its own area so you can access it efficiently. This is what Hadoop does in a nutshell: parallel processing with distributed storage. 

Hadoop Ecosystem: Core Components

Here are the core components of Hadoop:

Hadoop Distributed File System (HDFS)

HDFS is the primary data storage system used by Hadoop applications. It handles large data sets and runs on commodity hardware—affordable, standardized servers that are easy to buy off the shelf from any vendor. HDFS helps you scale single Hadoop clusters to thousands of nodes and allows you to perform parallel processing.

MapReduce

Known as the central processing of Hadoop, MapReduce splits data into smaller sets. It works based on two functions: Map and Reduce, which sparse the data efficiently and quickly. The Map function groups, sorts and filters multiple data sets in parallel to generate tuples (key, value pairs). In contrast, the Reduce function aggregates the data from these tuples to generate the desired output.

Yet Another Resource Negotiator (YARN)

YARN is used for cluster resource management. It works with the data you store in HDFS, allowing you to perform tasks such as:

• • Batch processing
  • Stream processing
  • Interactive processing
  • Graph processing

YARN is responsible for dynamically allocating resources and scheduling application processing. It supports MapReduce, along with several other processing models. YARN utilizes resources efficiently and is backward compatible, which means it can run on previous versions of Hadoop without any issues.

Hadoop Common

This component refers to the collection of shared libraries and utilities that support other Hadoop modules. It contains the necessary Java Archive (JAR) scripts and files to start Hadoop. Also, the component provides source code and documentation and has a contribution section that comes with different projects from the Hadoop Community.

How Does Hadoop Work?

The main function of Hadoop is to process the data sets in an organized manner among the cluster of commodity software. To process any data, the client should submit the program and data to Hadoop. HDFS then stores the data while MapReduce processes them. YARN divides the tasks. Here’s a look at how Hadoop works in detail:

1. The HDFS divides the client input data into 218 MB blocks—the smallest unit of data in the file system. Replicas of blocks are then created according to the replication factor. The blocks and their replicas are stored in different DataNodes (also called Slave nodes). 
2. Once the blocks and replicas are stored in the HDFS DataNodes, the user can begin processing the data. 
3. To process the data, the client sends the MapReduce program to Hadoop. 
4. The program submitted by the user is then scheduled by the ResourceManager on individual nodes in the cluster. 
5. Once processing has been completed by all nodes, the output is written back to the HDFS. 

Benefits of Hadoop

Here are some of the benefits of leveraging Hadoop:

• Fault-tolerant: Hadoop replicates data into different nodes to make sure that it’s available in case a system crash suddenly occurs. 
• Flexible and scalable: From unstructured to structured, Hadoop can process all kinds of data. It’s also scalable—you can add and subtract clusters depending on the company’s needs. 
• Fast: Hadoop was designed to address the speed issues that come with using conventional database management systems. Since it uses clusters to process data in parallel, it can deliver insights faster than expected.  
• Cost-efficient: Hadoop is economical because it uses a cluster of commodity hardware to store large data sets. Commodity hardware is cheap, so the cost of adding nodes to the framework is not very high.

When implemented effectively, Hadoop can be highly effective at processing Big Data. It’s a versatile tool for organizations handling large amounts of data.