Hadoop’s Processor is Rising the Speed of Big Data Technologies

Aren’t we lucky enough to be born in this Big Data age ? Yes we are, and we should make use of this immense opportunity by staying updated towards every little thing that happens around us in this tech era. Here is another chance for you guys to know something interesting related to Big data. In this work  of mine, I will be presenting to my readers how Hadoop technology can make data run even faster.

Big data has got the capability for challenging against its storing, searching, analyzing as well as its visualizing tasks. Big data is not popular only because of its volume but also because of the unpredictable way it analyzes data. The big data analytics remove low-valued data from any data sets and discloses the high-valued data. Big data is reaching its height of popularity and technologies are evolving rapidly in order to analyze these incredible analytics. Technologies related to Hadoop, such as Hadoop MapReduce, Hive, MapReduce etc are improving in order to lend a hand in the analysis of big data.

Since the data taken for computation is really big and heavy, so the performance of analytics has to be distributed across a large number of devices so that the computation is finished within the required time. Equalizing and paralleling the computation will reduce the original complex computation of the large sets of data with difficult codes. As an answer to this complexity, it is projected a new concept that allows us to convey the undemanding computations we were trying to carry out but covers the untidy particulars of parallelization, fault-tolerance, data sharing and load balancing in a data set arrangement.

For processing and generating large data files, Hadoop MapReduce is applied. By MapReduce, it means that the map takes care of the words present in the particular nodes and by reducing  it collects all the words associated with the intermediate key pairs. Codes which are written in MapReduce programming language are automatically distributed, paralleled and then only worked on a huge number of devices. After programming and compiling, it is seen that the time taken for calculating the words in the total data set without using MapReduce takes more than that using the MapReduce.

• The HDFS stores the huge sets of data in big clusters across the machines. It stores the data in blocks of about 64 MB each.
• The MapReduce is the processing part of Hadoop. It analyzes the data stored in the HDFS in the following ways:

Mapping Process

When the user program calls the MapReduce function, the following sequence of actions will be followed.

• When we use Hadoop MapReduce, the Massive data is broken into smaller pieces. It breaks the data input of 20 megabytes into 100 megabytes of each piece. Then the program is duplicated into a large number of copies and then distributed to the different machines.

• Out of all the copies, the original one is called as the Master program and the remaining copies are workers who work under the master. Counting the word count comes under the Map task and reducing the similar words of the same intermediate keys falls under the reduce task. The Master program assigns each duplicate program working under it with a Mapping task and also a Reducing task.

• The duplicate program who was given the responsibility of the mapping task, reads the complete input data and then splits the key pairs of the input file and takes each single pair and then transfers them to the user-defined Map Function. The in-between key pairs created due to the mapping task are then cushioned in the memory.

• The key pairs which are buffered in the memory are then stored into some of the local disks. Once these key pairs are stored, their addresses are then sent to the original program that is to the Master. The Master then assigns its workers with reducing tasks by giving them the address of each buffered pair. The worker now reads the data stored in the buffered key pairs using their addresses.
• The worker reads the data and then filters it related to the same intermediate key pairs and then shortens them up. This is essential because the concurrence of data relating to the same key pair is common.

Reducing Process:

• The worker during the reduce process works again and again on the merged data. Whenever it finds an exclusive key, it transfers the data related to that exclusive key to the reduce function of the user. This reduced result is the final outcome of the reducing process.

• After completion of both the tasks of Hadoop MapReduce, the user program is called back to the user code. Users do not have to work separately on the R files. All that they have to do is passing these files as an input to the next MapReduce call and thus the data will be processed by breaking up into multiple files. This is how Hadoop by using its processing part i.e. the MapReduce computes and analyzes the complicated sets of Big data.

These are the ways how Hadoop makes data run faster. If any of my readers have got any ideas please do share with us by writing down in the comment box mentioned below. We are glad to welcome your suggestions.