Big data computing often poses major difficulties for many companies. To address this problem, many organizations use tools such as software-based frameworks. These include the Java-connected Hadoop.

What is Hadoop?

The Java-based Hadoop Software Framework is the easiest to think of as a kind of shell that can be adapted to a wide variety of architectures and operated by a wide variety of workers, in this case the hardware.

The framework was invented by Doug Cutting, who developed Hadoop into one of the best projects in the field of the Apache Software Foundation until 2008. Cutting developed the software framework for better management of distributed and scalable systems. It builds on Google’s MapReduce algorithm, which uses Hadoop to combine large amounts of data into detailed computing processes on distributed but networked computers as a bundle.

Hadoop is not only so popular, but also because it is provided as a free source code free of charge for everyone by Apache and is additionally written in the well-known programming language Java.

What role does Hadoop play in big data?

Hadoop’s expertise in not only structured but also fast processing of big data, regardless of any kind, makes the software framework an attractive tool for many companies. In particular, the ability to present data from different sources with different structures in parallel in a bundle of clear and tangible is a great enrichment, especially for organizations in the business intelligence industry.

In addition, Hadoop makes it possible to efficiently solve complex computing tasks in the petabyte area and, on the basis of this, for example, to develop new business strategies, to collect basic information for important decisions or to significantly simplify the reporting of an organization.

Building

Hadoop consists of several building blocks, which in harmony make all basic functions of the software framework possible.

These are:

Hadoop consists of individual components. The four central components of the software framework are:

  • Hadoop Common,
  • Hadoop Distributed File System (HDFS),
  • MapReduce algorithm
  • Yet Another Resource Negotiator (YARN).

Hadoop Common is responsible for the basic functions and thus also serves as a basis for all other tools, such as the Java archive files. Connected to the other elements, Hadoop Common is connected via interfaces with defined access rights.

The Hadoop Distributed File System is used to store the individual data strains on different systems. According to the manufacturer, the HDFS is capable of managing data in the hundreds of millions.

Hadoop is powered by Google’s MapReduce algorithm. This allows the software framework to distribute complex computing tasks to various systems, which then process them in parallel. This can dramatically increase the speed of data processing.

The MapReduce algorithm is complemented by the Yet Another Resource Negotiator. The YARN manages the individual resources by assigning their tasks in the respective clusters.

Functioning

As mentioned earlier, Hadoop builds significantly on Google’s MapReduce algorithm. In addition, central tasks are also controlled by the HDFS file system, which is responsible for distributing the data to the individual bundle components. Google’s MapReduce algorithm, in turn, splits the processing of the data so that it can run in parallel on all bundle components. Hadoop then brings the individual results together into a large overall result.

Hadoop thus divides the data sets into individual clusters. Each cluster has a single master (represented by a computer node) while the other computer nodes are subject to that in slave mode. The slaves serve as a storage location for data, while the master is responsible for replication, making the data available on multiple nodes. Thanks to its ability to accurately determine the location of a data block at any time, the master protects efficiency from data loss. It also assumes the role of monitor of each node, which automatically accesses and re-replicates and stores its data block in the event of prolonged abstinence of a node.

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I blog about the impact of digitalization on our working environment. For this purpose, I present content from science in a practical way and show helpful tips from my everyday work. I am a manager in an SME myself and I wrote my doctoral thesis at the University of Erlangen-Nuremberg at the chair of IT Management.

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