Big data analytics is the realm of data scientists. Over the years, companies have realized that they have generated more data than they could possibly store and economically deal with. It is the fastest output that comes out of any properly accounted business, specifically in IT. It is important that this data serves its purpose before we run out of inventories to store them. One of the recent developments in data science is the utilization of fast processing speeds. It is possible to break down massive data sets into smaller compact sets. These smaller pieces can be distributed over a network or multiple networks which simultaneously work on it. The results are then aggregate and visualize. The entire process is call Big Data Analysis, and several companies are investing in this technology.

Apache Hadoop is also a recent technology that has allow big data analysis to be conducted efficiently and in a productive form. It is a bunch of utilitarian processes that make use of cluster networks to process the distributed data. These processes involve simplification of bigger data sets using MapReduce methodologies. Many companies such as IBM, Google, and Microsoft have made use of this application in their own analytical tools such as Watson, GCS, and Azure. Hadoop is widely acknowledged in the industry, and many educational training companies provide interest candidates with the necessary skills to become certifie in using the application for enterprise purposes. It is constantly growing and developing as its user base increases.


There are many real-world applications of Hadoop due to its various features such as scalability, reliance on big data hadoop, and tolerance towards errors. However, most of the applications delve around huge chunks of data that need processing.

  1. Hadoop has a huge role to play in forecasting and prediction using data. It helps companies to make sense of their sales information, profits data, and customer feedback to find the necessary insights that can boost their business and production schedule. Many companies make use of cloud computing services to achieve this expansion. Some of the popular cloud services are made available by Sprintzeal, Google, and Amazon themselves. Apache Hadoop framework is a great integration to such projects as it enhances the visualization of data and processing powers. The distribute networks are a great help in speeding up the forecasting process since a lot of variables have to be take in the equation, and regression is impossible through manual procedures.
  2. The healthcare industry has made tremendous utilization of this technology as well. Pharmaceutical companies and hospitals have made their patient information subject to data analysis which has resulted in many patterns to be discover regarding the spread of diseases and their cures. It has allowed us to better diagnose patients and suggest them specific and effective cures for their problems. In the long run, it helps us focus on the production of medicines and relevant products which are high demand, and the spread of critical diseases. The chances of epidemics are reduce.
  3. It is technically impossible for risk analysts to detect security risks to a system when there a lot of unknown variables involved. Big data analysis and Hadoop applications have made it possible to detect breaches and note their effects in real-time. This has severe security implications because now we can detect criminals and black hat hackers without jeopardizing data and waiting for older results to take shape. We can process data as it is and find footprints that previously went unnoticed. Law enforcement is greatly benefit from this development, and companies are able to reduce their risk recuperation costs as well.


As already mentioned, many companies help individuals to become equipped with the necessary knowledge required to conduct big data analysis. Simplilearn, Cloudera, and Edureka are some of the most popular online resources. These Big Data certification courses come as a part of many skill paths. Such as becoming data engineers and data analysts. They make use of different tools like Hive and. Spark to be able to provide the practical skills to the candidate. While some of the courses are training oriented. Some rely on examinations and practical assessments to test the capability of the individual.

Since Hadoop has so many applications in a wide range of fields. It is understandable why the skills are in such high demand. Big data analysis has helped in solving economic, healthcare, and productivity-related issues. The models that we have built using this technology keep getting better with the integration of other technologies. Such as machine learning.

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