What is Hadoop?
Hadoop has Hadoop Distributed File System (HDFS) (for handling stored files) and Map Reduce (that processes stored information) as its two main features. However, what is it? The answer is, a software framework used for large data sets optimized for its processing. Moreover, terabytes can easily be stored by HDFS through commodity servers that are not expensive. Large files are broken down into blocks with a default size of about 64 MB, but 128MB is more commonly and widely used. After that, each block’s copies are stored which are mostly three in number on different servers so that there is no significant loss of data in case of hardware failure? HFDS stores provide and even manage the data blocks that together form a file.
Coming towards its other feature that is Map Reduce, it permits applications that the data blocks can be processed not serially but in parallel. It all happens when the application segments run on the same server where the data blocks are mainly already situated.
If for example, a customer made many payments over a ten-year fee and the application needs to find the amount of those payments which means that much time traditionally will be taken if the required data is to be picked from a massive number of records through sorting out. Through Map Reduce on the other hand, each server will be assigned a specific block of files so that they will process just that. Same calculations by other servers on other blocks will be done simultaneously. The results are then consolidated with Map Reduce, so the answer from the application comes in very fast compared to the traditional method.
The significance of Hadoop:
Other than its efficiency, the importance of Hadoop lies in being a popular option for data storage. Storing of data and generating by many companies and businesses are rising day by day and each one of them is looking for ways to proficiently and lucratively organize the Additionally, many small and even large enterprises already use Hadoop to manage enormous amounts of data that would be hard to maintain otherwise. However, the question that still needs to be answered is, do we need Hadoop or we are good without it as well? Let’s figure it out.
Is Hadoop feasible for data and budget?
Hadoop analyses large sets of data that are unstructured and that being its primary function, it very quick in this matter and facilitates quite a lot so it can be said that Hadoop is handy when it comes to handling big data which is hard to organize customarily. One more question that one might think of is if real-time is required by your data or real-time analysis that is close. Well, processing of large datasets is completed in no time with Hadoop, and it undoubtedly excels in this area. Moreover, it is important to note the growing rate of data storage requirements. One of the best parts about this software is that it is very scalable so new storage capacity can be added with ease just by the addition of server nodes in the cluster of Hadoop (this group can expand when required with storage hardware and servers of low cost).
However, budget matters:
It depends on how much money the company is willing to spend. Yes, it is cost-effective, but Hadoop administrators and third-party support additionally are required as well. Analyst staffs and developers are supposed to be trained in to too along with the cost of converting the processes into Hadoop jobs. It surely depends on how much you are willing to spend as the price comes beyond the individual cost of cluster hardware.
Hadoop can still prove to be the best solution if you own a big business or are a part of some company that works with the vast amounts of data as well as low-cost storage budget. That shows that objectives of storage and processing of a group are obtained through this software. You sure do need it if you fall in any of the categories mentioned above to simplify up your work.