Difference Between Big Data and Apache Hadoop

Big Data: It is huge, large or voluminous data, information, or the relevant statistics acquired by the large organizations and ventures. Many software and data storage created and prepared as it is difficult to compute the big data manually. It is used to discover patterns and trends and make decisions related to human behavior and interaction technology. 

Application and usage of Big Data:

• Social Networking sites like facebook and twitter.
• Transportation like Airways and Railways.
• Healthcare and Education systems.
• Agriculture Aspects.

Apache Hadoop: It is an open-source software framework that built on the cluster of machines. It is used for distributed storage and distributed processing for very large data sets i.e. Big Data. It is done using the MapReduce programming model. Implemented in Java, a development-friendly tool backs the Big Data Application. It easily processes voluminous volumes of data on a cluster of commodity servers. It can mine any form of data i.e. structured, unstructured, or semi-structured. It is highly scalable. 

It consists of 3 components:

• HDFS: Reliable storage system with half of the world data stored in it.
• MapReduce: Layer consist of distributed processor.
• Yarn: Layer consist of resource manager.

Below is a table of differences between Big Data and Apache Hadoop: 

No.	Big Data	Apache Hadoop
1	Big Data is group of technologies. It is a collection of huge data which is multiplying continuously.	Apache Hadoop is a open source java based framework which involves some of the big data principles.
2	It is a collection of assets which is quite complex, complicated and ambiguous.	It achieves a set of goals and objectives for dealing with the collection of assets.
3	It is a complicated problem i.e. huge amount of raw data.	It is a solution being processing machine of those data.
4	Big Data is harder to access.	It allows the data to be accessed and process faster.
5	It is hard to store the huge amount of data as it consists all form of data. i.e. structured, unstructured and semi-structured.	It implements Hadoop Distributed File System (HDFS) which allows the storage of different variety of data.
6	It defines the data set size.	It is where the data set stored and processed.
7.	Big data has a wide range of applications in fields such as Telecommunication, the banking sector, Healthcare etc.	Hadoop is used for cluster resource management, parallel processing, and for data storage.