Apache Hadoop

Difference Between Bigdata and Hadoop

In this article, we will understand the very basic question which the beginners in the field of Big Data have. That is What is the difference between Big Data and Apache Hadoop.

1. Introduction

The difference between Big Data and Apache Hadoop is distinct and quite fundamental. But most of the people especially the beginners are sometimes confused between the two.

Big Data is simply the large set of data, large in a way that it can not be processed or stored by the traditional database system and can not be processed by traditional computing engines.

Let us first define both Big Data and Apache Hadoop so that we can understand the difference better.

2. Big Data

Big data is the term which has huge meaning and is sometimes used as am umbrella term for the whole ecosystem, this is where the confusion begins. So let us define Big Data is the simplest possible way:

Big Data is a large set of data that is so complex and large that it can not be processed by the conventional data processing application and can not be stored using the traditional database systems.

Big Data is generally describe to be having the following 3 properties:

  1. Volume: The volume of the data should be very large, large enough that a single machine can’t handle processing this volume.
  2. Velocity: The speed with which the data arrives is very high. One example being continuous streams of data from sensors etc.
  3. Variety: Big data can consist of multiple formats of data including Structured, Semi-structured and completely unstructured.

3. Apache Hadoop

Apache Hadoop is based on the Google’s MapReduce framework. It was implemented as the open source alternative to the Google’s MarReduce. Apache Hadoop is what is used to process Big Data. In the simplest terms, Apache Hadoop is the framework in which the application is broken down into a large number of small parts. These parts then run on the different nodes in a cluster of systems. This provides the capabilities to process the big data in a possible way using a cluster of multiple systems connected together and then aggregating the results to reach a final single set of result.

But now, many years after the release of Apache Hadoop, it is mostly used as an umbrella term for the whole ecosystem of frameworks and application which are used for storage, processing, analysis of big data. The current ecosystem consists of the Hadoop Kernel, Hadoop MapReduce, The Hadoop Distributed File System and the number of related projects like Apache Spark, Apache Storm, Hive, Pig etc.

There are two main components of the Hadoop framework though:

  1. HDFS: Hadoop Distributed File System (HDFS) is the open source equivalent of Google File System. It is the distributed file system which is used to store the big data on different systems in a cluster which will be processed by Hadoop.
  2. MapReduce: MapReduce is the actual framework which is used for processing of the data stored in HDFS. As we discussed the Map component processed the incoming data and the Reduce component reduces the processed data into a single set of result data which can be used by the user.

4. The Difference

Now as we have discussed and explained both Big Data as well as Apache Hadoop, let us see the difference between the both and how they are different from each other.

  • Big data is nothing but just a concept which represent the large amount of data and how to handle that data whereas Apache Hadoop is the framework which is used to handle this large amount of data. Hadoop is just a single framework and there are many more in the whole ecosystem which can handle big data.
  • Big Data is an asset often complex and with many interpretations whereas Apache Hadoop is a program that accomplishes a set of goals and objectives.
  • As Big Data is just a collection of data, it can consist of multiple formats of data while Apache Hadoop is the framework where is need to be handled and different code need to be written to handle different formats of data which can be structured, semi.structured and completely unstructured.
  • Apache Hadoop is an open-source framework maintained and developed by the global community of users. It includes various main components like MapReduce and HDFS and various other support components like Hive, Pig etc.
  • For analogy, Hadoop is a processing machine and big data is the raw material which is fed into this processing machine so that the meaningful results can be achieved.

5. Conclusion

Big Data can be defined as a “catch all” word related to the power of using a large amount of data which can be used to solve problems. Big Data jargon is a little confusing and can not be related directly especially for the beginners. I hope this article helps people to understand and distinct between the both. For more articles and deep understanding of the concepts, you can check other articles on our Big Data and Apache Hadoop section

Raman Jhajj

Ramaninder has graduated from the Department of Computer Science and Mathematics of Georg-August University, Germany and currently works with a Big Data Research Center in Austria. He holds M.Sc in Applied Computer Science with specialization in Applied Systems Engineering and minor in Business Informatics. He is also a Microsoft Certified Processional with more than 5 years of experience in Java, C#, Web development and related technologies. Currently, his main interests are in Big Data Ecosystem including batch and stream processing systems, Machine Learning and Web Applications.
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6 years ago

Thanks to @Raman Jhajj for this wonderful post to know about Hadoop and Bigdata.
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4 years ago

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4 years ago

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