Hadoop Security Issues

An important characteristic of Hadoop is the partitioning of data and computation across many (thousands) of hosts, and the execution of application computations in parallel close to their data. A Hadoop cluster scales computation capacity, storage capacity and I/O bandwidth by simply adding commodity servers. Hadoop clusters at Yahoo! span 40,000 servers, and store 40 petabytes of application data, with the largest cluster

Hadoop is a free, Java-based programming framework that supports the processing of large data sets in a Parallel and distributed computing environment. It makes Use of the commodity hardware Hadoop is Highly Scalable and Fault Tolerant. Hadoop runs in cluster and eliminates the use of a Super computer. Hadoop is the widely used big data processing engine with a simple master slave setup. Big Data in most companies are processed by Hadoop by submitting the jobs to Master. The Master distributes the job to its cluster and process map and reduce tasks sequencially.But nowdays the growing data need and the and competition between Service Providers leads to the increased submission of jobs to the Master. This Concurrent job submission on Hadoop forces us to do Scheduling on Hadoop Cluster so that the response time will be acceptable for each job.

Listed below are the concepts that should be learned in order to properly understand HADOOP technology.

In an attempt to manage their data correctly, organizations are realizing the importance of Hadoop for the expansion and growth of business. According to a study done by Gartner, an organization loses approximately 8.2 Million USD annually through poor data quality. This happens when 99 percent of the organizations have their data strategies in place. The reason behind this is simple – the organizations are unable to trace the bad data that exists within their data. This is one problem which can be easily solved by adopting Hadoop testing methods which allows you to validate all of your data at increased testing speeds and boosts your data coverage resulting in better data quality.

Data is a forever growing thing and is not looking to slow down anytime soon. With that be said, companies must be able to manage data for their day to day operations as well as for predicting future trend. Now there are many tools for managing data and extremely high cost for managing it as well. One of the costs for managing data is data security, but this paper will touch on that topic in, but not as much as other topics. Also, this paper will demonstrate various business cases to serve as testimonial for just how useful big data management it in to day society.

A significant paradigm shift is represented by public cloud computing from conventional norms of an organizational data center to a de-parameterized infrastructure which opens gates for potential adversaries to use. Cloud computing should be approached carefully with any emerging information technology area with due consideration to the sensitivity of data. A good planning helps and ensures that the computing environment is secure to the most possible extant and is in compliance with all relevant policies of an organization and makes sure the privacy is maintained.

The main purpose of this report is to provide a critical review of the processes and own experiences of Hadoop within the context of the assignment which was given to us. The review concentrates on the discussion and evaluation of the overall steps followed during the progress of the project and the reasons for which we have chosen these particular steps. It also draws attention at the main points that were accomplished, both with respect to individual, and with respect to the group 's perspectives. Finally, it concentrates on the project 's progress in terms of changes for a future implementation.

The main issue that slows down the growth of cloud computing is security. No matter how many security management tools are released or assurances of reliability are made, complications with data privacy and data protection continue to plague the market. Covering all potential security issues in this article is simply impossible. This is because of the fact that we 're still discovering many of the security issues which challenges cloud computing as it is still a work-in-progress. Cloud Computing is rapidly evolving and hence what we see today may quickly become irrelevant.

Most organizations have constrained and inefficient resources to lay down good IT systems and infrastructure. They do not respond promptly to customer needs that keep changing radically and make data sharing difficult. This problem can be solved through adoption of cloud computing; The cloud service providers should ensure that they consider the security issues (software,

The rise of Big Data and its attendant complexities has spawned a whole ecosystem to support the ever growing requirements of a 24x7 world. One of the key technologies coming out of the initial stages of Big Data has been Hadoop. Conceived in response to the rapidly growing needs of Yahoo!’s search engine, Hadoop provides a mechanism to store and collect vast amounts of data across a highly distributed environment using commodity hardware.

Big Data is creating great opportunities for businesses, companies and many large scale and small scale industries. Hadoop is an open-source cloud computing and big data framework, is increasingly used in the IT world. The rapid growth of Hadoop and Cloud Computing clearly indicates its importance as a Big Data enabling technology. Due to the loopholes of security mechanism, the security issues introduced through adaptation of this technology are also increasing. Hadoop services do not

Hadoop is an open source framework that could be very resourceful in data processing of the complex data systems, and has been reverently used in the recent past for query processing in the complex databases that contains millions of records. The major advantage of Hadoop is that it clusters the entire records to few blocks and the query is run on each cluster and the compiled information is displayed in effective terms.

A.I. has been an up-and-coming topic recently having received multiple TEDtalks on the subject and having been argued about from some high profile individuals on social media along with a few experiments between two A.I. systems which, contrary to the popular news stories, did not get shut down due to apparent issues with the new language the machines created (Novet, 2017). With the near limitless possible applications of A.I. in today’s society, one that stands out to me is it’s potential to assist with the field of IT to include in particular cloud computing and network security. A.I. has the potential to help with how network security evolves to deal with external threats and how cloud computing can gather and store the information

Cloud Computing and Big Data technologies can effectively be used to change the way in which businesses operate. Companies operating in fields such as financial, educational, technological and many more, have adopted the use of these technologies or are exploring the possibilities to become part of this new trend, as the benefits are proven to be a tremendous success. Not only for the increase in profits, but these technologies have a positive impact in many areas of a business. The success of Cloud Computing and Big Data has transformed businesses around the globe and “forced” them to spend billions on cloud computing services. The popularity of these technologies is rapidly growing and they are now accessible not only to giant companies that would spend limitless amount of money in order to have the best technology available, but also to small and medium size companies with a less significant budget.

Over the years it has become very essential to process large amounts of data with high precision and speed. This large amounts of data that can no more be processed using the Traditional Systems is called Big Data. Hadoop, a Linux based tools framework addresses three main problems faced when processing Big Data which the Traditional Systems cannot. The first problem is the speed of the data flow, the second is the size of the data and the last one is the format of data. Hadoop divides the data and computation into smaller pieces, sends it to different computers, then gathers the results to combine them and sends it to the application. This is done using Map Reduce and HDFS i.e., Hadoop Distributed File System. The data node and the name node part of the architecture fall under HDFS.