Enterprise Class Hadoop and Streaming Data †MyAssignmenthelp.com

Question: Discuss about the Enterprise Class Hadoop and Streaming Data. Answer: Introduction: The following assignment is the analysis of the big data threats and the steps which is taken to prevent the threats of the big data. The given assignment discusses about an organization known as the European Union Agency for Network and Information Security (ENISA) the center of network and the expert in the Information technology securities threat which assist the European nation bodies. The ENISA organization works with the members of the EU in assisting the securities related to the IT. The ENISA is also responsible for improving network and provide security to the entire European nations. The Big data is the collection of the huge data which is not only complex and not useful the purpose of the big data is to derive some pattern or the algorithm from these data to study the pattern which may include the behavior of the people related to certain companies (Kimwele, 2014). The source of the data can be from anywhere like the application of the internet of things or telecommunicati on and it can be in huge amount. The application of the big data is increasing every day and a more advanced and matured technique is getting developed to use the big data to derive the information (Kitchin, 2014). Many companies have admitted that the application of the big data has been huge and it has increased the effectiveness and efficiency in making the decision in the business. It is expected that in the coming days the demand of the big data is going to increase in all the sectors of the company be it a healthcare, banks, markets specially in for the military purpose and intelligence system. However despite of all its business there is various risks associated with the big data which is the prime targets of the hackers to retrieve the information. There are various threats related to the big data some of them are: Through the usage of the big data not only the original data but the confidential data are also at risk as with the high replication of the big data for purpose to store and the outsourcing of the big data these type of the technology are new ways of the breaching and the leakage of the data. The big data are posing threat to the privacy of the individual which has the impact on the data protection. In the big data at the time of the creation of the link at the collection of the data is the major cause of the penalization the extra creation of the link is the major cause of the leakage of the information and data. Thirdly different stake holders of the big data like the service providers data transformers, data owners have different opinion about the usage of the data thus their idea of usage of the data may conflict which makes its a difficult environment to opiate upon. Thus in such difficult envenom t the security of the data may be compromised. Lastly in many areas of the information and communication technology (ICT) .They are applying own security and privacy which is the best practice according to them but it would relatively decrease the all over security and the privacy risk related big data area(Walker 2014). As still in the early stage of the big data the rising pattern is embracing the Security-by-default principle which has proved to be both practical as compared to the effort and cost in term of time and money required to provide ad hoc solutions for the problem later on. Lastly the assignment analyses how there are huge gap between the problems related to the big data and the counter measures to tackle the problems of the big data. Therefore the assignment discuses about the lack of the proper countermeasures of the big data and how important is to take correct counter measures so that the next generation can also utilize the application of the big data. Therefore in the particular a valid question rises whether the current trends of the countermeasures for taking up the existing solutions which is against the data threats in the Big Data which mainly focuses on the amount of the data. The current countermeasures are made which is mainly to counter the scalabilities of the big data which does not fit the big data problems which results in the partial and ineffective approach to the protection of the big data(Walker 2014). The assignment enlists some of the guidelines and approaches for the countermeasure of the next generation of the data. Some of the recommendations enlisted are as remarked by the assignment are: I) to stop following the current approach to the traditional data and work on defining the Big Data related solutions ii) To find and identify the gaps and required needs for the current practices and to work in planning the specific definition and the specific standardization activities. To work in training and teaching the IT profession nals about the big data and teach they correct measures of usage of the big data. iv) To work in defining the correct tools and measures for security and privacy for the protection of Big Data and it environments v) To clearly find and identify the assets to the Big Data and to simplify the selection of solutions mitigating risks and threats (Walker 2014). Various major threats have been listed by the assignment. Some of them are: Threat due to Information leakage and sharing because of the human error Threat due to Leakage of the data through the Web applications (unsecure APIs) Threat due to inadequate planning and design or incorrect adaptation. Threat due to inception of the information. Above all the threats described by the assignment the significant threat can be leaks of the data via Web applications (unsecure APIs). As all the threat described by the assignment is a human error and it can be corrected with proper attention but the breaching of the data due to Web applications (unsecure APIs) is the breach which can take place due to the software which do not have enough capability to protect the data (Halenar, 2012). According to the assignment various sources has claimed that the security is of the less concern while building the big data. The new software components designed for the protection of the big data is usually built with the authorization of the service level, but there are few utilities which is available to protect the core features and application interfaces (APIs) (Kayworth Whitten, 2012). As we know that the big data are built and designed on the web services models. The application interfaces (APIs) become a prime target to well-known cyber at tacks, like the Open Web Application Security Project (OWASP). Which is in the top ten lists and there are few countermeasures to tackle them. The vendor of the security software Computer Associate (CA) and various other related sources find out via report that the data breaches are due to not a secure application interfaces (APIs), in various industries such as the photo and video sharing services and especially the social networks which includes the Face book, Yahoo and Snap chat (Chen Zhang, 2014). An example can be demonstrated the threat in the social networks and can be the injection attack to a company known as the Semantic Web technologies through the injection of the SPARQL code. The flaws in the security are very common in the newly available big data languages such as the RDQL and the SPARQL where both are read-only query languages. The utilization of the newly designed query languages has introduced the vulnerabilities which was already present in the misuse of the quer y languages of the old-style. The attacks on the languages like SQL, LDAP and Path are well known and dangerous for the usage. The libraries of this new language have been given the tools to check the user input and simultaneously minimizing the risk. There are other big data software products for an example Monod, Hive and Couched who also suffers from the traditional threats which includes the execution of the code and the remote SQL injection. The assets targeted by these threats belong to group Data and asset type Storage Infrastructure models (such as Database management systems (DBS) and Semantic Web tools). According to the assignment the threat agent is someone who has the clear intention and decent capability to plant a threat regarding the usage of the big data systems (ODriscoll, Daugelaite Sleator, 2013). It is crucial for the users of the big data to be aware of these threats and prevent them in their system. The various categories of the threat agents are Corporations: The category of the cooperation comes under the organizations which are involved in the tactics. In this category the cooperation are considers as the threat agents whose motivation and aim is to gain a competitive advantages over their competitors which is their main target. Depending on the cooperations size and its sector these corporations usually acquire the significant capabilities which range from the technology in the area of their expertise. Cyber criminals: Another threat agent is the cyber criminals the main target of these cyber criminals is to financial gain and they hack and breach the data of the company which is confidential and has a high demand in the market. These cyber criminals opiates on all levels be it a local or a global operator. Cyber terrorist: the main differed between the Cyber criminals and the Cyber terrorist is that the motive behind the data breaching is not only financial but also political, religious or social gain over the people (Labrinidis Jagadish, 2012). They basically operate at the global level to spread terror in the country and target the critical infrastructure o the country which includes the health and financial sector of the country which cause severe impact in society and government. Script kiddies: they are basically unskilled or incapable hackers who use the programme and scripts of the hackers to hack the computer system. Online social hackers (activists): The hackers who target the social media are politically and socially motivated individuals which use the platform to promote their believing and causes They mainly targets small children and girls to torture them mentally and promote cyber bullying in the social media. Employees: This category of the threat agent involves the people who works for the particular organization and have access to the data of the company. They mainly are the security guards or the operational staffs who are bribed by the individuals to steal the data (Zikopoulos Eaton, 2011). Nation states: Nation state is the rising threat which has become prominent in the recent times. The Threat agent which are due to the deployment of sophisticated attacks which are considered as cyber weapons with the capability of these malware (Provost Fawcett, 2013). The above threat agents identified implies that to avoid these threats people while using the social media should not post their private details such as the photograph or location as it can use by the hackers to cyber bully the users. Secondly the company needs to keep tab on their employees of their activities against and stealing of the data. The ETL is full form is extraction, transformation, and loading. ETL is the process in which the data is extracted from the source system and brought into the data warehouse. The first step is the extraction where the data is extracted from the various sources and is assembled in the certain place the second step is the transformation phase where the data is transformed according to the target requirement and last phase is the loading phase it is the phase in which the data is loaded into its warehouse and ready for the delivery (Boyd Crawford, 2012). For the better performance of the ETL various steps are enlisted: Loading the data incrementally: For the proper management of the data it should be arrange in certain matter which can be increasing or decreasing or any order according to the user need it will help in better management of the data and it will to find the record afterward as the user will remember the pattern (Tankard, 2012). The partition of the large tables: Using the relational database its use can be improved by the partition of the large tables when the large table data are segmented into the smaller part it will help in quicker and efficient access of the data (Singh Khaira, 2013). It will allow easier switching of the data and quick insertion, deletion and updating of the table. Cutting out the extra data: sometimes the table of data can become complex due to presence of the unwanted data. Therefore the table should be properly analyzes and the not required data should be eliminated to make the table more simple and easily accessible. Usage of the software: various software like hardtop and the map reduce which is designed for the distributed processing of large data over a cluster of machines (Al-Aqrabi et al 2012). It uses the HDFS application which segments data into the small part and make them into simple cluster. The data which is duplicated through which the system maintains the integrity automatically. According to the case study European Union Agency for Network and Information Security (ENISA) which is center of the network and the expert in the Information technology securities threat which assist the European nation bodies is currently not satisfied with the IT securities that is followed in the world it has enlists the various securities which includes the usage of the big data which is not only the original data but the confidential data which are at risk as with the high replication of the big data for the purpose of storage and the outsourcing of the big data these type of the technology are new ways of the breaching and the leakage of the data. Secondly the big data are posing threat to the privacy of the individual which has the impact on the data protection (Ackermann, 2012). The assignment also enlists 5 major threats related to the data mining. The assignment also enlists threat agent such as cooperation, people, cybercrime which spreads the online hacking. The assignm ent finally enlists countermeasures made mainly to counter the scalabilities of the big data which does not fit the big data problems which results in the partial and ineffective approach to the protection of the big data (Ackermann et al 2012). Thus the assignment clearly explains the current counter measures for the big data is not enough and should implement better strategies to avoid the misuse of the big data. References Ackermann, T. (2012).IT security risk management: perceived IT security risks in the context of Cloud Computing. Springer Science Business Media. Ackermann, T., Widjaja, T., Benlian, A., Buxmann, P. (2012). Perceived IT security risks of cloud computing: Conceptualization and scale development. Al-Aqrabi, H., Liu, L., Xu, J., Hill, R., Antonopoulos, N., Zhan, Y. (2012, April). 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