Master thesis mapreduce

Thesis and Research Topics in Big Data

The Big Data Hadoop architecture consists of the following components:. Big Data technology has emerged a lot in the past few years. Many companies are adopting the big data technologies to study the data patterns which will help in the growth of their business by identifying new opportunities. With big data analytics, businesses can make faster and better decisions.

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Moreover, it also helps in getting valuable customer insights regarding what they want and what are their needs. All these things make big data important for businesses. Big Data finds its applications in different areas such as retail, finance, health, media, telecommunication, e-commerce to develop better marketing strategies.

How to oppose and defend a master thesis

Following are the main applications of big data:. The concept of Hadoop has already been discussed earlier that it is an open-source framework for processing and managing big data. It is a good area to look for good thesis topics in big data.

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  4. Thesis and Research Topics in Big Data | Thesis in Big Data and Hadoop;
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A single Hadoop cluster has a single master node and multiple slave nodes. It also provides high aggregate bandwidth across the cluster. The importance of the Hadoop lies in the fact that more than half of the data produced is unstructured. This technology is required to optimize that data properly. Following are the advantages of Hadoop:. MapReduce is an algorithm for processing huge amount of data. This algorithm has two tasks to perform — Map and Reduce. In Map task, a set of data is taken and converted into another form by breaking down it into tuples.

In the Reduce task, the output of the Map i. The Reduce job follows the Map job. It is also a good field for research and thesis under Big Data.

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Two primitives mappers and reducers are used in the MapReduce model. The mapper processes the input data while the reducer process the data from the mapper. The MapReduce Model has the following main components:. Tools are used to analyze and process the data. Like Apache Hadoop, there are various other big data tools to manage data which is so large that it even exceeds terabytes in size. The big data tools are categorized on the basis of storage and processing.

You can pick up a tool and start your research in that. Following are the top tools used in Big Data Analytics:. Big Data Analytics is expected to see a fourfold increase in the near future and these tools are going to help the companies for processing and analyzing the data. It depends upon the company regarding which tool to use for their business.

Master's Thesis

It is one of the hot topics in Big Data for thesis and research. It is the language for data exploration and development. R and Hadoop can be integrated together in the following ways:.

No doubt big data is one of the emerging technologies, there are also security concerns of this technology. Big Data security is a term used to represent all the measures, practices, and tools used to protect the data from malicious attacks or thefts.


It is an interesting area for research and thesis in big data. The threat can be either online or offline. To protect the data it is best to implement big data security. The main security concerns in big data include corrupting of the incoming data, threat of stored data being stolen, third part attack. Encryption technique is used to protect the data from these security problems.

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  • Masters thesis, Dublin, National College of Ireland. With the increasing adoption of cloud-based infrastructure the problem of efficient utilization of provisioned resources becomes more important, since even in a pay-as-you-go model computing resources are allocated and charged in a coarse grained way e. This problem becomes major in the batch processing systems, where computational resources are organized into a cluster. Even small optimization of applications running on such systems can result in significant cost savings.

    In this thesis we evaluate one such application - JournalProcessor, which is a batch log-processing job that aggregates and indexes logs containing metrics data.