Security Issues Of Hadoop Services

Despite the numerous advantages offered by cloud computing, security is a big issue concerned with cloud computing. There are various security issues and concerns associated with cloud computing, among them being phishing, data loss and data privacy. There are

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.

The author points out that although there are existing algorithms and tools available to handle Big Data, they are not sufficient as the volume of data is exponentially increasing every day. To show the usefulness of Big Data mining, the author highlighted the work done by United Nations. In order to further enhance the reader’s perspective, the author provided research work of various professionals to educate its readers about the most recent updates in Big Data mining field. The author further describes the controversies surrounding Big Data. The author has first provided the context and exigence by elaborating on why we need new algorithm and tools to explore the Big Data. The author used the strategy of highlighting the logos by mentioning the research work of different industry professionals, workshops conducted on Big Data and was able to appeal to connect to the reader’s ethos. The author also used pathos by urging the budding Big Data researchers to further dig deep into the topic and explore this area

With the buzz around many big data applications, privacy concerns regarding their uses have also grown. With the personal data has been mined and published every day, the battle to reclaim the privacy starts vigorously. E-commerce websites harvests information about all the online searches of customers. Social Media exposes the likes and preferences of people, their photos and all their daily activities. Video surveillance monitors the movement of people. The data gets published from health care, censes and other government agencies. With such potential of harvesting, mining and publishing data, the risks of using big data is more than anything [29].

There are many vendors that are using Hadoop technology to improve their business but in this paper I will be talking about only three of them, such as Amazon Web Services Inc (AWS), Pivotal Corp and Datastax Inc. AWS own and maintain the network-connected hardware required for these application services, while you provision and use what you need via a web application. AWS is a provider of cloud computing, which refers to the on-demand delivery of IT resources and applications via the Internet with pay-as-you-go pricing. Pivotal Corp offers a modern approach to technology that organizations need to thrive in a new era of business innovation. Their solutions intersect cloud, big data and agile development, creating a framework that

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.

Cloud computing is an emerging paradigm that intends to provide on demand services through the internet (Chang & Tsai, 2008). In environmentally friendly power vitality perspective, the four aspects of cloud computing include low cost, large data storage, efficiency and reliability. Chang and Tsai (2008) incorporated the hadoop platform in cloud computing together with the access security under the influence of techniques such as finger print identification and face recognition. Most cell phones are served either through wired or through remote medium by hadoop cloud computing. Furthermore, most of slave nodes are controlled and connected by a master node which is located in the architecture defining Hadoop system where services such as PaaS, SaaS and IaaS are provided (Chang & Tsai, 2008).

Big data is a process in which transforming audits is conducted. Along with this, some major challenges are also adopted in order to maintain the standards as well as to give training in the required processes and systems. It is also said and impossible to further corroborate that around 75% of the world’s data has been initiated as well as created in the past two to three years. The actual and well organized proliferation of the entire data has changed our lives, and the way we act as consumers too. Therefore it is correct to mention that this era of big data helps in reshaping the operations of the businesses and it helps in giving them greater insights as compare to the early days. Therefore this is one of the

The big data has application in the detection of fraud. The banking industry acquires data concerning clients from the global world. The information analyzed from various sources makes the banks ensure they do not transact with the unauthorized individual. The data helps the bank to engage in business transactions with the credit worth clients who have not been declared bankrupt (Jerome, 2014).

Due to the rapid growth in the use of Internet and its connected tools, an enormous amount of data are being produced on a daily basis. The concept of big data arrives when we were unable to manage this huge data with traditional methods. Big data is a mechanism of capturing, storing and analyzing the big datasets and also an idea of extracting some value from it. It is very handful while determining the root causes of failures, issues and defects in near-real time, creating coupons and other sales offers according to the customers shopping patterns, detecting any suspicious and fraudulent activities in real-time. As it is very advantageous, it also has some issues. Some of the common issues can be characterized into heterogeneity, complexity, timeless, scalability and privacy. The most important and significant challenge in the big data is to preserve privacy information of the customers, employees, and the organizations. It is very sensitive and includes conceptual, technical as well as legal significance.

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.

Almost all kind of large and small organizations might face increasing number of attacks into their network or intellectual property. This may lead to data disclosure, data destruction, and damage of organization’s reputation. There are numerous threats in the cyber space which might be capable of stealing, destroying or making use of out sensitive data for financial and non-financial gains. As the amount of computer, mobile and internet users increases, so does the number of exploiters.

The evolution of Information Technology is inevitable, not only on the telecommunication and networking industry, but, it is also gaining more popularity in the business industry by way of Enterprise Resource Planning (ERP) system and rapidly making its move towards the accounting and auditing industry through Data Analytics Software, however, as the IT innovation continues and the concern of capacity and storage progress, and the demand for expansion and accessibility, brings in cloud computing and big data analytics. As the business industry grow after the financial crisis, companies examine opportunities for operational cost reduction and lower the risk of technology infrastructure becoming obsolete. Obviously integrating business functions to more highly technical and sophisticated system is cost effective, however the damage caused by security and privacy risk can be very costly as well. Guillot (2013) indicated that nowadays the inexpensive, effective and efficient business functions was owed to the evolving technology, but technology also makes the fraud easy to facilitate and engineer by perpetrators who can commit fraud anywhere and anytime as the internet, mobile devices, computers and the cloud are used to conduct business (p.43). Guillot quoted Steve Mar, director of IT statement “It’s not that fraud has changed, it’s that technology has made it easier” (p.43).

In Hadoop environment, data from various sources is first loaded and stored in the HDFS framework from the local file system. Map Reduce provides supports distributed and parallel I/O scheduling, it is fault tolerant and supports scalability and has inbuilt processes for status and monitoring of heterogeneous and large datasets as in Big Data. To fetch data from Hadoop File System, MapReduce program is used which is written in Java. The MapReduce program is executed and run in the system. The MapReduce program can be written in many ways as required by the programmer which is used for analyzing the data in different ways. The Mapper function can be run as