What Is The MapReduce Framework Used For?
Greg Black | February 19, 2010The MapReduce programming framework was developed by Google to process massive amounts of data in the most efficient way possible. In fact, it is often used when dealing with so much data that it requires distribution across (up to) thousands of machines to handle it effectively.
This kind of data processing doesn’t always have to be on such a large scale. Smaller companies can also make good use of this framework to organize data and discover new statistical relationships. MapReduce functionality will provide a method to analyze your data no matter how much or how litter there is.
Whether your data set is large or small, you can use a MapReduce application to query the system for very specific information. With the right information to work with, you will be able to manage fraud detection, work with graph analysis, explore sharing and search behavior, and monitoring the transformations. These are functions that were hard to manage, especially in data sets that were continually growing.
A MapReduce job, though, will split the input data set into smaller, more manageable jobs, which will then be processed by the map task in a completely parallel manner. The framework will then sort the output of the maps and put them into a reduce task. This is one of the best ways to utilize the resources of a large, distributed system.
After the information has been split and reduced, a user can employ MapReduce applications to deal with the rest of the processes. That means you can automate things like scheduling, monitoring, and any necessary re-executions of failed tasks. This will make any data mining activities much easier.
One option is to use the Hadoop API to interact with MapReduce functionality. You need to make sure that all data transfers and job configurations are correct and consistent in order to maintain the integrity of the data base. The API is the way that many companies are developing new and reliable methods to discover important facts in their data.
By using the Apache Hadoop API, you will be able to submit and configure your jobs with the job scheduler with ease. The scheduler with then distribute the appropriate tasks to the right worker systems within the cluster, as well as all the necessary monitoring tasks and produce various diagnostic and status reports as you go.
MapReduce functionality will allow you to simply your data processing across huge data sets and coordinate the activities that are necessary to derive valuable information. Whether you are using it to discover customer behavior or to organize all your important data, this programming framework is a good option for growing companies.
Working side by side with MapReduce, Hadoop API technology is a framework designed to go along with applications that need a lot of data. This technology can be confusing at times but ensures the tasks are completed properly.
