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How To Process Big Data With Hadoop Cluster on Amazon EMR?

How To Process Big Data With Hadoop Cluster on Amazon EMR
Article by guest Author
Jane Shel of Miromind
Miromind

How To Process Big Data With Hadoop Cluster on Amazon EMR?

For many years, organizations have been searching for ways to process their large amount of data. As an organization grows, there is a high rate of buildup of data. According to BedRock IT experts, such organizations would need to employ superior cloud-based data analysis techniques in order to manage the large amount of data. It is especially the case for organizations with unstructured data. Luckily, there are better solutions, which are not only reliable but cost effective when it comes to big data analysis. Hadoop cluster built on Amazon Elastic MapReduce (EMR) helps to process big data, which is also unstructured.

What is a Hadoop cluster?

It is a computing cluster that is specialized in the storage and analysis of big unstructured data. It makes this possible through a distributed computing platform where it has multiple cluster nodes where each node analyzes its own data. In short, the analysis workload is shared among the nodes, which makes the process fast and easy to complete. Hadoop clusters can be built on Amazon EMR, to help make big data processing efficient. EMR is an Amazon Web Services application which helps in processing and analyzing big data.

It provides a low configuration and high scalability which helps organizations to process big data without the need to build the system in-house. It works well with Hadoop cluster as data is processed across those cluster nodes which can be added depending on the amount of data that needs processing. MapReduce being an open source software platform, developers can develop data processing applications which can be run on the clusters to help with the fast processing of big data.

Processing big data with Hadoop cluster on Amazon EMR

Hadoop cluster provides the needed network on which cluster nodes can process and analyze data independently across the network. It offers a solution for businesses with unstructured data where they can process big data within a short period. With the help of Amazon EMR, organizations can achieve scalability in setting up the data processing system. Hadoop cluster utilizes multiple servers to process data, where there is the NameNode and the JobTracker, which are the masters. Other servers or clusters, Datanode and Tasktracker, are the slaves. Data is processed and analyzed across the clusters.

The Hadoop clusters process data at high speeds and are highly scalable. When there is too much data to be processed, you can add more nodes to increase the volume of data processing. Also, data is copied to the respective cluster nodes which makes it impossible for data to be lost while processing in the system, in the case of cluster node failure.

The advantages of using Hadoop cluster

Hadoop clusters utilize multiple cluster nodes in the processing and analysis of data, which makes it easy to process big data for organizations. Each node processes its data parallel across the network in a distributed manner. It helps to break down data into small bits where the analysis is shared among the cluster nodes. The Amazon EMR aids in scaling the framework based on the processing needs of an organization or client, by increasing or reducing resources.

Hadoop cluster built on Amazon EMR may not be effective in an organization that does not have unstructured and a large amount of data. In that case, the choice of the data processing and analyzing tool to use will depend on your organizational data processing needs.

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Jane Shel

Jane Shel

Jane Shel is a co-founder and content strategist at MiroMind SEO & Digital agency. Aside from her digital marketing know-how, she is a long-term green lifestyle enthusiast.