The advent of big data has created challenges for Data process and storage. The massive rise in the number of users of social media has accelerated the flow of information at a tremendous rate. The data flow and its storage have become challenging with the use of conventional methods. Hadoop was introduced in the market into store data storage and process obstacle that companies were facing. To get an insight into Hadoop, we need to understand big data first.
What is big data?
Big data encompasses dealing with large sets of data that are critical to the process and analyze systematically. It includes extracting useful information. It comprises oceans of data generated from various sources in a structured, unstructured and semi-structured format. The use of conventional stools and techniques fails to process such large datasets and thus emerges the need for advanced technologies such as Hadoop to store, process, analyze data, and draw useful information. In the present time, data is generated from online transactions, social media platforms, and other sources. It facilitates the enterprises to arrive at better decisions and prepare strategies to stay ahead in the competition.
Big data is attributed with the following characteristics.
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Volume: The magnitude of data highly effects in determining the value of data. It is of the volume of information and datasets that it is categorized as big data. It is one of its attributes, and this data comes mostly in an unstructured format. This data can be from clickstreams on webpages, social media data feeds, clicks from web apps, and many more.
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Variety: In an enterprise, data emerge from various sources. It can be structured and unstructured format. In the conventional format, the data appeared in the format of spreadsheets and documents, unlike now, which is in the format of images, emails, videos, and many more. The variety of data poses numerous challenges that range from storage, preprocessing, and so on.
Need for Hadoop
To counter the emerging needs for processing and managing big data. In the primitive ways, tools were used to store data and gain useful insight, but with the advent of big data, enhanced means for the big data needs are required, Thus Hadoop here comes into existence, it caters to the present market needs and provide framework to find the usable information by segregating data into large blocks and distribute into nodes in cluster. It is designed with the vision that fault occurrence is common, thus in case of any failure. It is handled by the framework by transferring the data to other nodes in the cluster. Today organizations are hiring Hadoop professionals to meet the increasing requirements of data handling and data analysis.
Introduction to Big Data Hadoop
Hadoop is an open-source framework to store and process big data in the distributed matter on the clusters. It is a top priority for most organizations. It is licensed under the Apache foundation. It was developed by Dug Cutting and Michael J. Cafarella on paper issued by Google on MapReduce. This framework is written in Java programming language.
Core Components of Hadoop
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