Difference Between Big Data and Hadoop

Difference Between Big Data and Hadoop

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Rashmi
Rashmi Karan
Manager - Content
Updated on Nov 26, 2021 18:01 IST

As predicted by IDC, global data volume grew from 4.4 zettabytes to 44 zettabytes between 2013 and 2020. By 2025, IDC predicts that there will be 163 zettabytes of data from mobile devices, Internet of things devices with information sensing, remote sensing, software logs, cameras, microphones, RFID readers, and wireless sensor networks. When we talk about big data, Hadoop often comes into the picture and people use them interchangeably, however, there is a difference between big data and Hadoop, let us check out.

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Big Data

The term Big Data refers to large data sets. Such huge volumes that it gets necessary to use specific techniques and tools to deal with them. Due to its characteristics of size, speed of growth, and variability, traditional technologies and methods are not enough to manage big data efficiently.

Among these computer tools designed to handle large amounts of data is specific software, generally distributed and capable of scaling with the volume and speed at which the data is generated. Current usage of big data includes predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from big data. However, there is no specific data size defined for a set of data to be called Big Data.

Importance of Big Data

This generation of massive data and its storage, processing, and analysis has become critical for many organizations, being one of the sectors with the most growth and professional trajectory today. The Big Data sector is expected to multiply its valuation in the market by 4 times by 2025, including the internet of things, cloud computing, artificial intelligence, and automation.

The value that organizations can extract from this data is focused on its use for making better strategic decisions, developing mathematical models, artificial intelligence, etc. In many cases, the analysis of the data obtained by an organization can give clues and ideas about new problems, and answer questions based on objective information, which increases security and confidence.

Hadoop

Hadoop is an open-source framework with which any type of massive data can be stored and processed. It has the ability to operate tasks in an almost unlimited way with great processing power and get quick responses to any type of query about the stored data. The main purpose of the framework is to store large amounts of data and allow queries on said data, with a low response time. This is achieved through the distributed execution of code in multiple nodes (machines), each of which is in charge of processing a part of the work to be done.

Apache Hadoop Components

The basic components of Apache Hadoop are –

Hadoop Distributed File System: The information is not stored on a single machine, but is distributed among all the machines that make up the cluster.

MapReduce Framework: MapReduce is a systematic approach that uses the HDFS distributed file system for the parallel processing of data. The system is structured through a master-slave architecture where the master server of each Hadoop cluster receives and queues user requests and assigns them to the slave servers for processing.

Advantages of using Hadoop

Some remarkable benefits that Hadoop offers, include –

  • Developers do not have to face the problems of parallel programming
  • Allows to distribute the information in multiple nodes and execute the processes in parallel
  • It has mechanisms for data monitoring
  • Allows data queries
  • Has multiple functionalities to facilitate the treatment, monitoring, and control of the stored information

 Difference between Big Data and Hadoop

 

Big Data

Hadoop

Definition Refers to a huge chunk of structured and non-structured data. It is raw data containing mainly user-generated content to be analyzed It is an open-source framework required to manage that data. Based on a distributed software framework to handle huge data set storage and processing across clustered servers
Value Has little or no value until processed One of the different tools to store, process, and analyze big data
Accessibility Difficult to access given its size Allows to access and process the big data very fast
Storage Not possible to store big data because of its raw and unstructured form Hadoop Distributed File System (HDFS) is the primary data storage system in Hadoop, storing big data
Nature Big data is considered an asset Just a tool to pull out value from the asset
Type Consists of multiple formats of data Clusters different formats of data which can be stored as structured, semi-structured, and completely unstructured
Applications Used in fetching information from –

  • Social Networking sites like Facebook, Instagram, and Twitter
  • Public transportation
  • Healthcare and education systems
  • Agriculture
Used in –

  • Fraud detection and prevention in finance
  • Detect and prevent cyber-attacks
  • Understand user behavior from huge data sets
  • Real-time analysis of customers data
  • manage content on social media platforms
Scalability A complex set of data that is open to interpretation and can be unscalable Allows to scale the system as the volume of data received grows, since to process more data

Conclusion

Through the knowledge extracted from big data analysis using tools like Hadoop, organizations are able to find new trends. This adds a lot of value and allows them to come up with viable and effective solutions at a higher speed. Hope this article helped in clearing the doubts regarding the concepts of big data and Hadoop and the difference between big data and Hadoop. Keep reading and learning!

About the Author
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Rashmi Karan
Manager - Content

Rashmi is a postgraduate in Biotechnology with a flair for research-oriented work and has an experience of over 13 years in content creation and social media handling. She has a diversified writing portfolio and aim... Read Full Bio

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