Big Data and Hadoop are the two commonly used terms in the field of business and data science. But only a few people know about their purpose and the difference between them. So, today we are going to learn about both these terms in detail and see what is the difference between them.
Basic Difference between Big Data vs Hadoop
Hadoop is a great open-source framework that allows its users to easily store and process large datasets. The framework allows its users to cluster multiple computers for storing and processing large datasets. The framework contains four main modules:
- Hadoop common
Big Data is a collection of large/complex data sets that are very difficult to process/store using traditional databases. These data sets are available in three different formats and are very difficult to process using traditional data processing apps. Furthermore, you may face lots of difficulties while analyzing, visualizing, sharing, and transferring your data. To learn about Big Data in more detail feel free to join the Big Data Hadoop Training Institute in Delhi.
Hadoop allows its users to easily process and access data easily and quickly in comparison to traditional data processing tools. While processing and accessing Big Data is not that easy using traditional data processing apps and databases.
Hadoop comes with robust data storage capabilities and allows its users to easily store large data sets. Whereas Big Data is very difficult to store. In simple words, it is a problem whereas Hadoop is the solution to that problem.
Hadoop enables its users to easily process large data sets and make them more meaningful and valuable. On the other hand, Big Data does not have any value until you process and analyze it.
Type of Big Data vs Hadoop
Big Data is basically a type of problem that has no use or value until you process and analysis it. While Hadoop is basically a type of solution that makes it very easy for its users to store/process huge data.
Using Hadoop, you can process and analyze data very easily for the purpose of decision-making. The data that is processed using this framework is highly trustworthy. On the other hand, you cannot rely upon Big Data for making important decisions. The data is useless and has no value unless you process and analyze it. Thus, it is right to say that Big Data is not that reliable when it comes to making important decisions.
Hadoop is very fast and enables its users to process data at a very fast rate. Using it a data scientist can easily process large data sets in a very short amount of time. While Big Data is very slow in comparison to its competitor.
Big Data is very vast and complex in nature and consists of a humongous volume of data. Additionally, it is of no use unless you process it. Whereas Hadoop is a tool in nature and enables its user to store and process large data sets very easily/quickly. Moreover, it enables/allows its users to bring out useful trends from raw data.
Hadoop is a very easy-to-use tool. You won’t face any problems while using it for processing and analyzing large data sets. On the other hand, working with Big Data is not easy. Furthermore, you may face lots of difficulties in processing, storing, and securing your data.
Hadoop is very easy to use and manage just like any other tool or program. While Big Data is not that easy to handle and manage as it contains a large quantity of data. To learn more about handling large data sets feel free to join the Big Data Hadoop Online Course.
There are lots of differences between Big Data and Hadoop. One is a problem while the other is a solution. Furthermore, both of them serve two different purposes too. However, both of them complement each other and can provide many advantages to an organization.