The MapReduce platform was created by Google to enable easier processing of large data in an intensive environment. It is based on multiple parallel processing of queries tasked in a large data environment. It eases the cost of implementation of database management and analytics software by using common computing elements as worker nodes for tasking. The advantages of using MapReduce are:
- One, simplified programming model. The process of using a distributed computing platform for data analysis, retrieval, organization, and related tasks is simplified into easy to understand tasking mechanisms. The end user needs only to have basic skills in database systems to implement and use MapReduce information. The platform is built on simple programming language such as C++ and Java. Furthermore, it is designed to allow the user choice in the selection of the native programming language they prefer to use. Complex mechanisms within the platform system are hidden from user view and access. This enables the user to access the system from a relatively easy to use base platform user interface. This simplifies the use and implementation in large data environments where tasks are data intensive.
- Two, it provides access to essential tools for database management and organization. Such tools are parallelization, load optimization, fault tolerance, and locality optimization. Parallelization refers to a multi-node parallel storing and computation process. This is especially useful in data environments as it decreases the latency of processing large data sets. Load optimization refers to the control of the resources within a network system to ensure that each node is tasked as per its capability. The load optimization tools enable to prevent chokeholds within the system architecture. This would lead to an imbalance within the spine of the system which can cause system collapse. Fault tolerance tools enable the system to withstand faults in data processing within the node architecture. Locality optimization is concerned with data loops and sequential codes.
- Three, it allows for easy expression of queries within the database architecture. The expression is what is processed as a task by the network architecture. This enables MapReduce information to be used by programmers who do not have specialized expertise in database management systems.
- Four, it increases the performance of the system by allowing the user to scale applications within the available clusters. Clusters refer to connected common computing elements within the system. Scaling enables the user to request more resources as they need them to process a task.