6 Reasons Why Hadoop is THE Best Choice for Big Data Applications

Often people ask us about what is big data? what is Hadoop? Where did it come from? and why it’s such a hot topic now a days. Well, after repeated answering same questions; I finally said, it’s better to write about it and share with even more folks out there. So, here is my attempt on putting 6 Reasons why Hadoop is THE Best Choice for Big Data applications and why it is so important to big data world today. Interested to know more, keep reading.

 

What is Hadoop

Hadoop is an open source distributed storage and processing software framework sponsored by Apache Software Foundation. The core technology used to build its framework is Java technologies as Java natively offers platform independence and wide acceptance across the world.

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Hadoop has become the foundation of big data applications

Hadoop is designed to work on commodity hardware and to advocate horizontal scaling of large software applications. Being open source, meaning its software is free to use by anyone and is designed to scale horizontally on need basis, puts Hadoop in a unique category of software frameworks. Being able to scale horizontally when need arises and free to use, are one of the prime reasons for its world-wide acceptance.

This is not all, but fundamentally Hadoop offers unique abilities to first store huge amounts of data and process that data in distributed manner using tens of thousands of machines in parallel and generating results in much less time than it would take to process same amount of data otherwise.

If you want to try hands on with Hadoop, then you should check out – Everything you need to know about Hadoop Shell

Next let’s review the 6 reasons why hadoop is the best choice for big data applications.

 

Quick side note, here is a list of related posts that I recommend:

• How to Quickly Setup Apache Hadoop on Windows PC – Step by step instructions on how to set up Apache Hadoop on windows PC within an hour.
• How to setup Apache Hadoop Cluster on a Mac or Linux Computer – step by step instructions on how to get started with apache Hadoop on a Macbook or Linux machine.
• What is MobaXterm and How to install it on your computer for FREE – If you haven’t used MobaXterm before, then I highly recommend you to try it today. I guarantee you will never look back.
• Learn ElasticSearch and Build Data Pipelines – This is actually an intro to a very comprehensive course on integrating Hadoop with ElasticSearch. This is one of key skills for advancing your data engineering career.
• How to avoid small files problem in Hadoop – an important article to improve performance of hadoop cluster and save it from lots of trouble.
• The Best Data Processing Architectures: Lambda vs Kappa – Choose the best architecture for your big data applications

 

This is what Hadoop Offers Out of Box –

1. Hadoop Handles Hardware Failures Automatically

From time to time, computer hardware failures will occur and they are unavoidable. Rather than spending energy on avoiding the hardware failures, Hadoop prefers to detect the failures as early in the stage as possible and perform automatic recovery from it.

In order to detect failures,Hadoop provides a feature called Heartbeat. In a typical master-slave architecture, where there’s one master machine managing many slave machines, master will receive a feedback or Heartbeat from slave machines every few seconds. If a slave missed to send Heartbeat two or three concurrent times, master will consider there’s something wrong with the slave and will start preparing another slave machine as exact replica of failed slave. This is autonomously done by Hadoop when it detects any hardware failures with a slave.

Hadoop keeps multiple copies of same data across different data nodes and racks to use for this purpose when time comes.

 

2. Hadoop Processes Large Data Volumes

Hadoop is designed to handle large, very large data sets in distributed manner. If dataset is small, it may not be distributed enough and may not benefit from parallel processing offered by Hadoop’s processing engine such as MapReduce. The system is designed to handle millions of files across tens of thousands of nodes across a single cluster. This is one of core reasons why hadoop is the best choice for big data applications and companies are adopting it across the globe.

 

3. Hadoop is Master of Distributed Processing

Distributed storage is not of much use if every time data needs to be moved across machines for processing. This was one of the bottlenecks in previous generation of large software applications. Hadoop moves the processing work to where the data is residing. This saves a ton of network bandwidth and time as processing (code) is almost always way smaller in size than the actual data that it has to process. This is another reason why hadoop is a the best choice for big data applications.

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4. Hadoop Ports  Big Data Applications without Hassles

The system is designed to work on commodity hardware. This enables it to be moved from one set of hardware to another without any major issues. This is why if a machine crashes in Hadoop’s cluster, it can simply be replaced by new machine. This provides an edge to Hadoop over its rivals and that’s why it’s the most adopted big data technology today.

 

5. Hadoop is a Batch Processor

Hadoop is designed for batch mode processing. What it means is that Hadoop is good for handling huge amounts of data in back-end operations rather in a real-time interactive system such as a website where a user would get the results instantly. In future, we expect this to change and hadoop to become more of a real-time interactive application.

This is how a high end server looks like that is used for hadoop like clusters.

6. Hadoop Works Well with Others

This is probably the most important factor for so much of success that Hadoop has achieved in recent years. The large community of its developers and companies keep launching new features and ways to integrate it with other technologies.

For instance, you can easily integrate ElasticSearch, Spark, Kafka, Sqoop, Storm, etc with Hadoop within few hours now. ElasticSearch is another hot technology used by many companies to create their own in-house data analytics tools and search engines. You can read more about how to integrate ElasticSearch with Hadoop.

According to DataShark Academy, these are the 6 reasons why hadoop is the best choice for big data applications today. Hadoop has a very active community of great developers working around the world to provide new features. So, new features and enhancements will keep coming in future but we believe core features won’t change much.

You might be interested in knowing how data engineers from 74 countries have decided to take this course. It is one of the hottest skill in demand today. You can check it out here.

Let us know your thoughts on what you think about this post. If you think there’s an important feature that we miss in above list, please let us know in comments.