If you have been handling or at least have the basic understanding of the technicalities of the Internet, you surely would be knowing that the vast space holds plenty of data which keeps piling up every day as the clicks go on increasing. Around 2.5 quintillion bytes of data is generated per day in structured and unstructured formats. This huge collection of data is what we call the ‘Big Data.’ It is the set of data that comes in large and complex formats, which is quite challenging to be processed and stored by employing the traditional database management tools and data processing applications. Among various the aspects that are difficult to achieve in the processing of ‘Big Data’ include the sharing, analyzing, visualization, searching, transferring, curating, storing, and capturing of data. The seven factors that highly influence the ‘Big Data’ are variety, volume, veracity, velocity, variability, value, and visualization.
Hadoop is what we are going to look into next. It is an open-source software framework that is being used to process and store the separate collections of commodity hardware in such a way that it remains distributed. Hadoop was licensed under the Apache v2 license and developed by the MapReduce system(Must Watch). Functional programming concepts have been applied here to make it one of the advanced projects by Apache that is written in Java.
Big Data vs. Hadoop
As you know, huge data requires a whole lot of space to be stored. Data comes in all formats including structured, unstructured, and semi-structured, of which, the latter two cannot be stored using the traditional database but can surely be done with Hadoop. This is the major difference between the two. Let us now have a look at more differences.
1. Accessibility
Big data is quite difficult to be accessed while the Hadoop framework makes it easier to process and access all forms of data faster than the other tools.
2. Storage
Big data is tough to be stored since it exists in both structured and unstructured forms, but Apache Hadoop HDFS is capable of storing big data.
3. Significance
The big data remains invaluable until it is processed to make some profit, whereas Hadoop has the ability to process these data to make it valuable.
4. Definition
Both of them fundamentally differ in their definition. While Hadoop is a framework that handles and processes the massive volume of data rushing in, ‘Big Data’ is simply a huge volume of unstructured and structured data.
5. Developers
The developers of ‘Big Data’ will only focus on developing an application in Hive, Map Reduce, Pig, and Spark. On the other hand, Hadoop aims at processing the data through coding.
6. Type
While ‘Big Data’ is a problem that stands invaluable when unprocessed, Hadoop is a solution that alleviates the issue of complex processing of big data.
7. Representation
‘Big Data’ is the vast space that contains a humungous volume of data, and it represents many technologies that are used for the processing stage. In contrast, Hadoop represents only a framework that implements certain principles for processing big data.