Big Data and Hadoop- A Match Made in Heaven

Big Data Hadoop

Why is Hadoop preferred for Big Data? You all know about Apache Hadoop, whose name was inspired by the cute toy elephant for kids. It goes beyond this sweet name and refers to an extensive project, open-sourced in nature offering businesses innovative ways to store and process Big Data. The framework for this software has been written in Java for distributed storage and processing of large data sets on computer clusters made from commodity hardware.

The Explosion of Big Data and Hadoop

Today, eminent web 2.0 companies like Facebook and Google deploy Hadoop for storing and managing huge sets of data (both structured and unstructured). However, besides being beneficial to them and other esteemed companies, Hadoop also offers value to traditional companies as well based on the following advantages.

Let us take a look at them below and know why Big Data and Hadoop are truly a “match made in heaven”- 

1. Scalability

Big Data Hadoop

Hadoop is an extensively scalable platform because it has the ability to store and distribute huge data sets across multiple inexpensive servers that parallelly operate together. Unlike the conventional relational database systems or RDBMS, that cannot scale to process large sets of data, Hadoop offers businesses the scope to operate applications on thousands of nodes with thousands of terabytes of data sets. 

2. Inexpensive and cost-effective 

Big Data Hadoop

Hadoop gives companies a storage solution that is cost-effective. Data sets are exploding today and traditional management RDBMS systems are expensive to scale to the level of processing massive data sets. In a bid to reduce costs, in the past, several companies would have to down sample the data for classifying it on specific assumptions as to which of this data has the most value. They had no choice but to delete the raw data. 

This approach might have been feasible in the short-term but today, especially in the Pandemic world, business priorities have changed. The complete raw sets of data are not available as it is very expensive for a business to store it. Hadoop, on the other hand, has been designed as an architecture that can be scaled out. It has the ability to store all of the organizational data for subsequent usage. 

The costs saved in the process are mind-blowing. Instead of costing companies thousands to tens thousands of rupees per terabyte, Hadoop gives you storage and computing capabilities for thousands of terabytes and petabytes conveniently. 

3. Flexible

Big Data Hadoop

Hadoop helps businesses to access new sources of data easily and tap into both unstructured and structured data for generating value from that data. This implies businesses can use Hadoop to get valuable insights from different data sources like email conversations, social media or clickstream data. Besides this, Hadoop can also be deployed for a wide range of reasons like recommendation systems, market campaign analysis, data warehousing and the detection of fraud. 

4. Fast


Hadoop has a storage method that is unique. It is based on a distributed file system that fundamentally maps the data irrespective of its location on the cluster. The data processing tools are on the same servers where it is located resulting on data processing that is super-fast. This means if you deal with large volumes of unstructured data, Hadoop can effectively process terabytes of data sets in just some minutes and petabytes of data in just a few hours. 

5. Resilience to failure 


Another advantage of using Hadoop is its solid ability for fault tolerance. When the data is transmitted to the specific node, it is replicated on the other nodes of the cluster. This means, in the event of failure, there is always another copy available for your use. The MapR distribution goes the extra mile by removing the NameNode and later replacing it with the No NameNode architecture promising high availability to the business. The architecture offers data protection from single as well as multiple failures seamlessly. 

Therefore, from the above, it is evident, that Hadoop has great efficacy when it comes to handling huge data sets in a cost-effective and safe manner. Hadoop has the advantages that other relational database management systems do not have when it comes to the collection and storage of Big Data. Its value is indispensable for any business irrespective of its size. It will continue to be popular as the influx of unstructured data continues to increase in the modern times.  

In the age of Big Data today, Hadoop has indeed paved the way for a unique approach to the challenges the former poses in the current era. When we refer to Hadoop, it does not imply to just the “Hadoop Framework” but the entire “Hadoop Ecosystem” as well. Here tools like Apache Hive that offers SQL similar operations along with Hadoop, Apache Hbase for columnar storage databases, Apache Pig, Apache Spark for in-memory data processing and several more. In short, it is highly adaptable and continuously evolving with every release- truly match made in heaven for Big Data.

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