Key Differences between Big Data and Hadoop

Data is widely collected worldwide. The large amount of data is known as Big Data and it is not handled by normal devices. According to Big Data Hadoop Online Training, The Hadoop software framework, which is an open source framework of the Apache Software Foundation, can be used to overcome this problem.

The key difference between Big Data and Hadoop is that Big Data is a large amount of complex data, while Hadoop is a mechanism to store Big Data effectively and efficiently.

What is big data?

Data is produced daily and in large quantities. It is important to store the data collected accordingly and analyze it for best results. Google, Facebook collects a lot of data daily. Organizing data and analyzing it can bring benefits to the organization. In a bank, it is essential to analyze the data to understand customer information, transactions, customer problems. Analyzing this data and developing solutions will improve profits. This shows that data plays a vital role for an organization to function efficiently and effectively. As data grows rapidly, relational databases or regular storage devices are not enough.

Big data has three properties. They are volume, speed and variety. First, Big Data is a large volume of data. This data can take the volume of Giga Bytes, Tera Bytes or even more than that. The second attribute is speed.

This is an important property in the analysis of environmental changes and for the detection of airplanes. The data must be accurate and continuous in those situations. It is a considerable factor to make decisions in real time. Another main property is the variety, which describes the type of data. The data can take text, video, audio, image, XML format, sensor data, etc.

What is hadoop?

It is an open source framework of the Apache Software Foundation for storing Big Data in a distributed environment for parallel processing.  The Hadoop storage system is known as Hadoop Distributed File System ( HDFS ). Divide the data between some machines. Hadoop follows the master-slave architecture. The master node is called node-name and the slaves are called data nodes . Data is distributed among all data nodes.

The main algorithm used to process data in Hadoop is called Map Reduction. Using map reduction programs, jobs can be sent to slave nodes. The default language for writing map reduction programs is Java, but other languages can also be used. The data nodes or slave nodes will perform the analysis task and send the result to the master node/node-name.

Master-node/name-node has a Job Tracker to execute map reduction jobs on slave nodes. Slave nodes/data nodes have a task tracker to complete the data analysis and send the result to the master node. Hadoop has some advantages. It reduces costs, data complexity and increases efficiency. It is easy to add another machine to the Hadoop cluster.

Differences Between Big Data and Hadoop

Big Data

• Big Data is a large collection of complex and varied data that is difficult to store and analyze using traditional storage methods.
• Big Data is difficult to store since it consists of a variety of data, such as structured and unstructured data.
• Accessing Big Data is difficult.

Hadoop

• Hadoop is a software framework for storing and processing big data effectively and efficiently.
• Hadoop uses the Hadoop Distributed File System (HDFS) that allows you to store a variety of data.
• Hadoop allows you to access and process Big Data faster .

The data is growing rapidly. All government and business organizations are collecting data. Analyzing data is extremely valuable. A single computer is not enough to store a large amount of data. This large amount of complex data is called Big Data.

Therefore, Big Data can be distributed among some nodes using Hadoop. The difference between Big Data and Hadoop is that Big Data is a large amount of complex data and Hadoop is a mechanism to store Big Data effectively and efficiently. Get to know more about such interesting topics of Hadoop by being a part of Big Data Hadoop Online Training.