A common query amid a lot of IT professionals is the difference between Big Data and Hadoop. The terms are often interchanged for one another as a lot of people fail to understand the difference between the two. The increasing popularity of Hadoop certification and Big Data has further added to the confusion.
The reality is that Big Data and the open-source Hadoop program are actually complementary to each other and cannot be compared. In simple words, you can think of Big Data as a problem and Hadoop program as a solution to resolve the problem. While Big Data is an ambiguous and complex asset, Hadoop is a program with the help of which a particular set of objectives can be achieved to deal with the asset.
Understanding the problems with Big Data and how Hadoop resolves them is a simple way to know the differences between the two.
Problems with Big Data
Big Data is defined with the help of 5 characteristics: Volume, Variety, Velocity, Value, and Veracity. Here, the volume is the amount of data; variety is the type of data, velocity is the rate at which the data is being generated, the value is the usefulness of the data and veracity is the amount of incomplete or inconsistent data.
Let us now have a look at two of the biggest problems with Big Data-
1. Storage- The traditional storage systems cannot store the colossal amount of data generated every day. On top of it, as the variety of data differs, it is important to store the data separately as per their type.
2. The speed of accessing and processing data- Just like the storage problem, the speed of accessing and processing data is a major issue with Big Data. While the hard disk capacities have increased significantly in the past few years, not much improvement has been made to the access speed and transfer speed.
Big data training is an excellent way to understand these problems in detail. Both these problems have been effectively resolved by Hadoop.
Overview of Hadoop
Hadoop is an open-source software program with the help of which the Big Data can first be stored in distributed environments and can then be processed in a parallel manner. Hadoop is made up of two important components- Hadoop Distributed File System (HDFS) and YARN, Hadoop’s processing unit.
Big Data solution with Hadoop
Let us now have a look at how Hadoop resolves both the major Big Data problems-
1.Storage- With HDFS, the Big Data is stored in a distributed manner. Everything is stored in datanode blocks, and you get to specify the size of every block. Moreover, it won’t just divide the data across different blocks but will also replicate all the blocks on the data nodes too. So, as commodity hardware is being used, storage is no more a problem with Hadoop.
Similarly, the problem related to different types of data has also been addressed with the help of HDFS as it can store all the different varieties of data. It also follows the ‘write once and read many’ model with the help of which you just need to write the data once and access it multiple times.
2.The speed of accessing and processing data- To resolve this issue, rather than the traditional method of moving data for processing, Hadoop moves processing to the data.
This means that the processing logic is moved across all the different slave nodes and parallel processing of data takes place throughout the slave nodes. Processed results then move to a master node where the merging of data takes place and the response created is sent to the client.
Comparisons can take place between something that is similar in nature. As you can see, there is nothing as such between Big Data and Hadoop, and they are just complementary to each other. While one is a problem statement, other is a solution to the problem.
The increasing popularity of Big Data and how it can benefit organizations has significantly increased the requirement for professionals with Hadoop certification. If you are planning a career in Big Data, look for a reputed training provider to get the certification and climb the ladder of professional success.
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