Hadoop technology

The magnificence of the Hadoop technology in handling the big data

Hadoop technology & Big Data

As the top line of the article proclaims regarding the concepts of  Big data and the Hadoop. Big data as the name suggests, is a collection of big datasets, where it will not be able to processed using usual computing techniques. When we think of Hadoop, it is an open source software program. Hadoop being open source software is a combination of online running applications on a large scale. The Hadoop will take the responsibility for handling and managing the big data in an effective manner. With the aid of Hadoop, one can manipulate and handle the big data in a fast and cost-effective manner. This is the era where Hadoop technology can stand as a king among other professions.

An overview of big data

We all can agree that the amount of data is enhancing by mankind in this era of new technologies, due to the advent of technologies, devices and communication through the social media like FB, Twitter and many more. Production of the data is increasing day by day rapidly. The amount of data produced by us in the year 2003 was 5 billion gigabytes and it’s really shocking to hear that, if we pile up the whole data in the farm of disks it may fill up an entire ground of football, the same amount of data was collected within span of two days in the year 2011. If we see the statistics it was the same amount of data which was collected in the year 2013 within 10 minutes, so one must get an idea that how crucial is to handle that huge amount of data.

The unique techniques and features of Hadoop

Hadoop being a framework operates and maintain, manipulate the big data. The appreciable thing of Hadoop is that it does not require the structured data. The unformatted data can be dumped by the users in the framework. Unlike relational database, the Hadoop does not need the well-structured schema prior to storage of data.

Hadoop’s programming models are simple, if the distributed systems are written in a complicated way, then they can be tested by using the simple programming model of Hadoop.

The administrative part of the Hadoop is the easiest; HPC (High-Performance Computing) is a system which enables the programs to run on the cluster of computers. There are chances of rigidity in the configuration of the program. The HPC clusters must be carefully administered since there are chances of a node failing at the time of execution of the program.

Guidelines for solving the issues of the Big data using the Hadoop

Open source software known as Hadoop changes the perception in handling the big data, especially the unstructured data. Apache Hadoop is a framework fetched from the Apache Hadoop software library, which enables excess of data to be consolidated for any distributed processing system across a bunch of computers. Apache achieves it by simple programming models. The main purpose is to scale up from single server to n number of machines. Each of those offers local computation and storage space. The Apache library has primarily created to support and manage breakdowns at the application layer, instead of depending on hardware to offer high-availability.

The Hadoop package comprises of:

  • JAR (JAVA Achieve) files
  • A Map Reduce engine
  • OS level abstractions and File system
  • Scripts needed to begin the Hadoop
  • The HDFS (Hadoop Distributed File system)
  • Source Code

Actions performed on Big Data:

Store: The big data need to be saved in a flawless repository, and it is not compulsory to collect in one physical database.

Process:  In terms of transforming, cleaning, manipulating, and running algorithms is more tiring process than the traditional one.

Access: If the data cannot be fetched, searched and can be practically showcased, then we can say there is no business sense in that.

As last readers can come can conclude that there are clusters of machines involved in solving the issues of big data and it can be done through Apache Hadoop. Apache Hadoop consists of distributed file system through this the data will get split and stored on the compute nodes, so the parallel processing of data is possible on the cluster machines.

Even though Hadoop is restricted to the batch process (Batch process can be defined as the execution of series of programs without manual intervention), it has proved as a mature framework to solve the issues of the Big Data like storage, processing and security related issues.

About: VaishnaviAG

Vaishnavi Agrawal loves pursuing excellence through writing and have a passion for technology. She has successfully managed and run personal technology magazines and websites. She currently writes for intellipaat.com, a global training company that provides e-learning and professional certification training. The courses offered by Intellipaat address the unique needs of working professionals. She is based out of Bangalore and has an experience of 5 years in the field of content writing and blogging. Her work has been published on various sites related to Data Science, Hadoop, Big Data, Business Intelligence, Project Management, Cloud Computing, IT, SAP, Project Management and more.

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