With the recent release of Hadoop 2, there is no doubt that a large chunk of office techies would shift to its usage in both small and medium sized businesses. The problem is that although techies have no problem grasping the many pros of having Apache’s Hadoop 2 in the system, some of the people in the managerial section may have a hard time understanding exactly what makes this new software special.
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How Hadoop Works
Businesses in general have more than 500 GB of information that can be too big or too valuable to store in a regular PC. Sometimes, the file size can be too big that it is virtually impossible to store it in just one server. This is where Big Data & Hadoop comes in. Basically, the software makes it possible for companies to store very large files quickly and efficiently. More importantly, it can store multiple amounts of very large files. The additional features of Hadoop 1.0 and more recently, Hadoop 2.0, are specifically designed to augment its main job of storing large amounts of data securely, while providing business owners with an easy access to the information.
Hadoop2 versus Hadoop1 Comparison
So what exactly makes Hadoop 2 different from Hadoop 1? First of all, Hadoop 2 comes with more functions which allow for a more extensive handling of applications. The second version’s architecture is also more complex than the previous one:
Hadoop 1.0
MapReduce
HDFS
Hadoop 2.0
MapReduce Others
YARN
HDFS
Hadoop 2 – A Big Leap in Massive Data Storage
Hadoop 1.0 is already a big deal when it comes to massive data storage, but Hadoop 2.0 brings it all to a more impressive field. Needless to say, Apache has paved the way for some innovations with the Hadoop 2.0 setup. You will find that there are new features to speak of, including but not limited to the following:
- YARN – this is the biggest and possibly the best addition in Hadoop 2.0. It stands for Yet Another Resource Negotiator and now takes over the function of JobTracker. It has been noted that YARN is like Hadoop’s operating system because it takes care of all operations including monitoring as well as managing different workloads.
- HDFS – although HDFS is present in Hadoop 1.0, there is marked improvement for the HDFS of the later version. It stands for Hadoop Distributed File System and its main function is to connect the different nodes and turn out a large file system. It covers all the nodes in the cluster and is mainly responsible for keeping all the valuable information together.
- MapReduce – this is another aspect of Hadoop that has been brilliantly improved. In Hadoop 1.0, MapReduce is the only available way of processing data. It is basically a system that helps move large data from one location to another. Unfortunately, not all types of data respond to MapReduce – hence the production of YARN. In the new Hadoop version, MapReduce is still present but simply considered as a component of YARN.
So why exactly is it considered a big leap with big data processing? See, the main problem with Hadoop 1.0 is the limitations when it comes to scaling. With Hadoop 2.0, this problem is completely eradicated as YARN becomes available. According to Apache, all applications that are distributed today can be accommodated by YARN, which is a tall order but definitely good news. They have even supplied a list of applications that are compatible with YARN.
Hadoop 2.0 – Real World Difference in Big Data
So, what do all these acronyms do in a real world setting? Remember, you still have to explain exactly what Hadoop 2.0 would do for the company. Aside from the obvious reason of making it easier for you to transfer and store large files easier, the following are some of the perks offered by Hadoop 2.0
- Cost Effective – using the software leads to parallel computing which essentially reduces the cost of storage per terabyte. This allows you to store more information without necessarily burning up too much space available for the business.
- Scale Function – another beauty of Hadoop 2.0 is the fact that you can add new nodes when it becomes necessary. Even better, there is really no need to change your formatting just to make room for a node – which means that you will be able to maintain the orderliness of your data even as you pile more on to it.
- Tolerant of Faults – another plus of Hadoop 2.0 is that it has become more tolerant to faults. Imagine having a problem with your node and instantly losing valuable data because of it. With the new Hadoop version, the system is automatically redirected when a node is lost, ensuring that the information is stored in a different location. As a result, you will never have to worry about losing your data during transfer.
- Accommodating – with the inclusion of YARN, Hadoop 2.0 has become more flexible. This translates to an improved ability of processing different data formats and accepting information from different sources. It can aggregate information, paving way for better analysis of your stored data.
- Compatible – if you are a user of anything else, then Hadoop 2.0 will certainly make a difference. It has been specially formatted to work with other programs created by Apache. Having it installed in your system means that everything else becomes easier to work with.
What about backward compatibility? Apache decided to cover all bases by utilizing the same framework with MapReduce found in Hadoop 2.0. This way, old jobs can still be processed but may need recompiling before it can work together with Hadoop 2.0.
Learning Hadoop
Since Hadoop 2.0 bears a strong resemblance to Hadoop 1.0, learning how it works should not be a problem as long as you have sufficient experience with Apache’s last software. If you are a complete newbie, however, it might take some time to completely understand how this works and to completely make the software work for you.
Today, there are a lot of Hadoop 2.0Big Data tutorials you can find online. For more information about Hadoop 2.0, check out Apache’s main page. Although it can be said that the latest version definitely presents some good stuff, it can be expected that further improvements are on the works.
