Big Data is basically a term that covers large and complex data sets. To handle it, one requires use of different data processing applications when compared with traditional types.
While there are various applications that allow handling and processing of big data, the base framework has always been that of Apache Hadoop.
Hadoop is an open-source software framework written in Java and comprises of two parts, which are the storage part and the other being the data processing part. The storage part is called the Hadoop Distributed File System (HDFS) and the processing part is called MapReduce.
We now look into the advantages that are offered by Hadoop MapReduce programming.
The advantages of MapReduce programming are,
Hadoop is a platform that is highly scalable. This is largely because of its ability to store as well as distribute large data sets across plenty of servers. These servers can be inexpensive and can operate in parallel. And with each addition of servers one adds more processing power.
Contrary to the traditional relational database management systems (RDMS) that cannot scale in order to process huge amounts of data, Hadoop MapReduce programming enables business organizations to run applications from a huge number of nodes that could involve the usage of thousands of terabytes of data.
Hadoop’s highly scalable structure also implies that it comes across as a very cost-effective solution for businesses that need to store ever growing data dictated by today’s requirements
In the case of traditional relational database management systems, it becomes massively cost prohibitive to scale to the degrees possible with Hadoop, just to process data. As such, many of the businesses would have to downsize data and further implement classifications based on assumptions of how certain data could be more valuable that the other. In the process, raw data would have to be deleted. This basically serves short term priorities, and if a business happens to change its plans somewhere down the line, the complete set of raw data would be unavailable for later usage.
Hadoop’s scale-out architecture with MapReduce programming, allows the storage and processing of data in a very affordable manner. It can also be used in later times. In fact, the cost savings are massive and costs can reduce from thousands and figures to hundred figures for every terabyte of data.
Business organizations can make use of Hadoop MapReduce programming to have access to various new sources of data and also operate on different types of data, whether they are structured or unstructured. This allows them to generate value from all of the data that can be accessed by them.
Along such lines, Hadoop offers support for numerous languages that can be used for data processing and storage. Whether the data source is social media, email, or clickstream, MapReduce can work on all of them. Also, Hadoop MapReduce programming allows for many applications, such as recommendation systems, processing of logs, marketing analysis, warehousing of data and fraud detection.
Hadoop uses a storage method known as distributed file system, which basically implements a mapping system to locate data in a cluster. The tools used for data processing, such as MapReduce programming, are also generally located in the very same servers, which allows for faster processing of data.
Even if you happen to be dealing with large volumes of data that is unstructured, Hadoop MapReduce takes minutes to process terabytes of data, and hours for petabytes of data.
Security and Authentication
Security is a vital aspect of any application. If any unlawful person or organization had access to multiple petabytes of your organization’s data, it can do you massive harm in terms of business dealings and operations.
In this regard, MapReduce works with HDFS and HBase security that allows only approved users to operate on data stored in the system.
One of the primary aspects of the working of MapReduce programming is that it divides tasks in a manner that allows their execution in parallel.
Parallel processing allows multiple processors to take on these divided tasks, such that they run entire programs in less time.
Availability and resilient nature
When data is sent to an individual node in the entire network, the very same set of data is also forwarded to the other numerous nodes that make up the network. Thus, if there is any failure that affects a particular node, there are always other copies that can still be accessed whenever the need may arise. This always assures the availability of data.
One of the biggest advantages offered by Hadoop is that of its fault tolerance. Hadoop MapReduce has the ability to quickly recognize faults that occur and then apply a quick and automatic recovery solution. This makes it a game changer when it comes to big data processing.
Simple model of programming
Among the various advantages that Hadoop MapReduce offers, one of the most important ones is that it is based on a simple programming model. This basically allows programmers to develop MapReduce programs that can handle tasks with more ease and efficiency.
The programs for MapReduce can be written using Java, which is a language that isn’t very hard to pickup and is also used widespread. Thus, it is easy for people to learn and write programs that meets their data processing needs sufficiently.
When it comes down the processing of large data sets, Hadoop’s MapReduce programming allows for the processing of such large volumes of data in a completely safe and cost-effective manner. Hadoop also triumphs over relational database management systems when it comes to the processing of large data clusters. Finally, many businesses have already realized the promise that Hadoop holds and it is imperative that its value to businesses will grow as unstructured data keeps growing.