Small to huge companies need proper data management strategies for assured success. Most recently, companies and organizations have found the need to process enormous amounts of data every day. Data processing is important for decision making and management. So, without compromise, organizations need reliable, efficient, and effective data processing systems to work with.
The technology that has proven to be very helpful while dealing with big data processing is the Apache Hadoop software. Basically, Hadoop is a technological breakthrough that allows storage and management of enormous data sets. By design, the data sets are distributed across clusters of servers from where analysis applications run.
What’s the technicality of Hadoop?
The software has two main parts. First is the distributed file system which is an array of storage clusters and is responsible for holding the actual data. Once the data has been captured by the system, it is safely stored for any future needs. The reliability of this system is enhanced by the fact that these distributed clusters are independent of each other and do not affect each other’s functionalities. This means that if one cluster or server fails, the system will sill continue functioning well.
The second part is the data processing framework & MapReduce. Storing data would be irrelevant if using it is not possible. The data processing framework allows for working with the data itself. It may involve retrieving, analyzing, and manipulating the data. As opposed to Database Management Systems (DBMS) which employ Structured Query Language (SQL), Hadoop employs MapReduce which is a Java-based system.
The MapReduce effect!
When a request is initiated, the Hadoop framework makes use of about every machine in the network with utmost efficiency. First, it uses a Job Tracker to subdivide the job and Map the pieces with respective machines where specifically needed information is stored. From the machine ends (nodes), Task Trackers are used to accomplish the specific tasks. Once solutions have been acquired for every subset, these solutions are Reduced back to the main server.
As noted earlier, the failure of one machine within the network would not compromise the system. However, on the same light, addition of a machine to the network would mean additional storage space as well as additional processing power. This makes Hadoop the best system for handling large amounts of data. Not to forget such qualities as flexibility and robustness that Hadoop possesses.
Complexity of The Hadoop System
The effectiveness of Hadoop on availing storage space and reducing processing time is excellent. In fact, due to that reason, this technology is the most popularly used. Many companies and organizations continue to embrace it when dealing with big data.
However, as opposed to DBMSs, Hadoop comes with a certain level of complexity attributed to its development, implementation, and maintenance. Mostly, this would affect learning since learners have difficulties getting their heads around this java-based framework. For any form of assistance regarding Hadoop, make sure to check out essaylions.com